Theoretical Aspects of the SOM Algorithm
Theoretical Aspects of the SOM
Algorithm
M.Cottrell†, J.C.Fort‡, G.Pagès∗
† SAMOS/Université Paris 1
90, rue de Tolbiac, F-75634 Paris Cedex 13, France
Tel/Fax : 33-1-40-77-19-22, E-mail: cottrell@univ-paris1.fr
‡ Institut Elie Cartan/Université Nancy 1 et SAMOS
F-54506 Vandœuvre-Lès-Nancy Cedex, France
E-mail: fortjc@iecn.u-nancy.fr
∗ Université Paris 12 et Laboratoire de Probabilités /Paris 6
F-75252 Paris Cedex 05, France
E-mail:gpa@ccr.jussieu.fr
Abstract
The SOM algorithm is very astonishing. On the one hand, it is very simple
to write down and to simulate, its practical properties are clear and easy to
observe. But, on the other hand, its theoretical properties still remain without
proof in the general case, despite the great efforts of several authors. In this
paper, we pass in review the last results and provide some conjectures for the
future work.
Keywords: Self-organization, Kohonen algorithm, Convergence of stochas-
tic processes, Vectorial quantization.
1 Introduction
The now very popular SOM algorithm was originally devised by Teuvo Kohonen
in 1982 [35] and [36]. It was presented as a model of the self-organization of neu-
ral connections. What immediatly raised the interest of the scientific community
(neurophysiologists, computer scientists, mathematicians, physicists) was the abil-
ity of such a simple algorithm to produce organization, starting from possibly total
disorder. That is called the self-organization property.
As a matter of fact, the algorithm can be considered as a generalization of the
Competitive Learning, that is a Vectorial Quantization Algorithm [42], without any
notion of neighborhood between the units.
http://arxiv.org/abs/0704.1696v1
In the SOM algorithm, a neighborhood structure is defined for the units and is
respected throughout the learning process, which imposes the conservation of the
neighborhood relations. So the weights are progressively updated according to the
presentation of the inputs, in such a way that neighboring inputs are little by little
mapped onto the same unit or neighboring units.
There are two phases. As well in the practical applications as in the theoretical
studies, one can observe self-organization first (with large neighborhood and large
adaptation parameter), and later on convergence of the weights in order to quantify
the input space. In this second phase, the adaptation parameter is decreased to 0,
and the neighborhood is small or indeed reduced to one unit (the organization is
supposed not to be deleted by the process in this phase, that is really true for the
0-neighbor setting).
Even if the properties of the SOM algorithm can be easily reproduced by simu-
lations, and despite all the efforts, the Kohonen algorithm is surprisingly resistant
to a complete mathematical study. As far as we know, the only case where a com-
plete analysis has been achieved is the one dimensional case (the input space has
dimension 1) for a linear network (the units are disposed along a one-dimensional
array).
A sketch of the proof was provided in the Kohonen’s original papers [35], [36]
in 1982 and in his books [37], [40] in 1984 and 1995. The first complete proof
of both self-organization and convergence properties was established (for uniform
distribution of the inputs and a simple step-neighborhood function) by Cottrell and
Fort in 1987, [9].
Then, these results were generalized to a wide class of input distributions by
Bouton and Pagès in 1993 and 1994, [6], [7] and to a more general neighborhood by
Erwin et al. (1992) who have sketched the extension of the proof of self-organization
[21] and studied the role of the neighborhood function [20]. Recently, Sadeghi [59],
[60] has studied the self-organization for a general type of stimuli distribution and
neighborhood function.
At last, Fort and Pagès in 1993, [26], 1995 [27], 1997 [3], [4] (with Benaim) have
achieved the rigorous proof of the almost sure convergence towards a unique state,
after self-organization, for a very general class of neighborhood functions.
Before that, Ritter et al. in 1986 and 1988, [52], [53] have thrown some light on
the stationary state in any dimension, but they study only the final phase after the
self-organization, and do not prove the existence of this stationary state.
In multidimensional settings, it is not possible to define what could be a well
ordered configuration set that would be stable for the algorithm and that could be
an absorbing class. For example, the grid configurations that Lo et al. proposed
in 1991 or 1993, [45], [46] are not stable as proved in [10]. Fort and Pagès in 1996,
[28] show that there is no organized absorbing set, at least when the stimuli space
is continuous. On the other hand, Erwin et al. in 1992 [21] have proved that it
is impossible to associate a global decreasing potential function to the algorithm, as
long as the probability distribution of the inputs is continuous. Recently, Fort and
Pagès in 1994, [26], in 1996 [27] and [28], Flanagan in 1994 and 1996 [22], [23] gave
some results in higher dimension, but these remain incomplete.
In this paper, we try to present the state of the art. As a continuation of previous
paper [13], we gather the more recent results that have been published in different
journals that can be not easily get-a-able for the neural community.
We do not speak about the variants of the algorithm that have been defined and
studied by many authors, in order to improve the performances or to facilitate the
mathematical analysis, see for example [5], [47], [58], [61]. We do not either address
the numerous applications of the SOM algorithm. See for example the Kohonen’s
book [40] to have an idea of the profusion of these applications. We will only mention
as a conclusion some original data analysis methods based on the SOM algorithm.
The paper is organized as follows: in section 2, we define the notations. The
section 3 is devoted to the one dimensional case. Section 4 deals with the multidi-
mensional 0-neighbor case, that is the simple competitive learning and gives some
light on the quantization performances. In section 5, some partial results about
the multidimensional setting are provided. Section 6 treats the discrete finite case
and we present some data analysis methods derived from the SOM algorithm. The
conclusion gives some hints about future researches.
2 Notations and definitions
The network includes n units located in an ordered lattice (generally in a one- or
two-dimensional array). If I = {1, 2, . . . , n} is the set of the indices, the neighbor-
hood structure is provided by a neighborhood function Λ defined on I × I. It is
symmetrical, non increasing, and depends only on the distance between i and j in
the set of units I, (e.g. | i − j | if I = {1, 2, . . . , n} is one-dimensional). Λ(i, j)
decreases with increasing distance between i and j, and Λ(i, i) is usually equal to 1.
The input space Ω is a bounded convex subset of Rd, endowed with the Eu-
clidean distance. The inputs x(t), t ≥ 1 are Ω-valued, independent with common
distribution µ.
The network state at time t is given by
m(t) = (m1(t), m2(t), . . . , mn(t)).
where mi(t) is the d-dimensional weight vector of the unit i.
For a given state m and input x, the winning unit ic(x,m) is the unit whose
weight mic(x,m) is the closest to the input x. Thus the network defines a map
Φm : x 7−→ ic(x,m), from Ω to I, and the goal of the learning algorithm is to
converge to a network state such the Φm map will be “topology preserving”in some
sense.
For a given state m, let us denote Ci(m) the set of the inputs such that i is the
winning unit, that is Ci(m) = Φ
m (i). The set of the classes Ci(m) is the Euclidean
Voronöı tessellation of the space Ω related to m.
The SOM algorithm is recursively defined by :
ic(x(t+ 1), m(t)) = argmin {‖x(t + 1)−mi(t)‖, i ∈ I}
mi(t + 1) = mi(t)− εtΛ(i0, i)(mi(t)− x(t + 1)), ∀i ∈ I
The essential parameters are
• the dimension d of the input space
• the topology of the network
• the adaptation gain parameter εt, which is ]0, 1[-valued, constant or decreasing
with time,
• the neighborhood function Λ, which can be constant or time dependent,
• the probability distribution µ.
Mathematical available techniques
As mentioned before, when dealing with the SOM algorithm, one has to separate
two kinds of results: those related to self-organization, and those related to conver-
gence after organization. In any case, all the results have been obtained for a fixed
time-invariant neighborhood function.
First, the network state at time t is a random Ωn-valued vector m(t) displaying
m(t + 1) = m(t)− εt H(x(t+ 1), m(t)) (2)
(where H is defined in an obvious way according to the updating equation) is a
stochastic process. If εt and Λ are time-invariant, it is an homogeneousMarkov chain
and can be studied with the usual tools if possible (and fruitful). For example, if
the algorithm converges in distribution, this limit distribution has to be an invariant
measure for the Markov chain. If the algorithm has some fixed point, this point
has to be an absorbing state of the chain. If it is possible to prove some strong
organization [28], it has to be associated to an absorbing class.
Another way to investigate self-organization and convergence is to study the asso-
ciated ODE (Ordinary Differential Equation) [41] that describes the mean behaviour
of the algorithm :
= − h(m) (3)
where
h(m) = E(H(x, m)) =
H(x, m) dµ(x) (4)
is the expectation of H(., m) with respect to the probability measure µ.
Then it is clear that all the possible limit states m⋆ are solutions of the functional
equation
h(m) = 0
and any knowledge about the possible attracting equilibrium points of the ODE
can give some light about the self-organizing property and the convergence. But
actually the complete asymptotic study of the ODE in the multidimensional setting
seems to be untractable. One has to verify some global assumptions on the function
h (and on its gradient) and the explicit calculations are quite difficult, and perhaps
impossible.
In the convergence phase, the techniques depend on the kind of the desired con-
vergence mode. For the almost sure convergence, the parameter εt needs to decrease
to 0, and the form of equation (2) suggests to consider the SOM algorithm as a
Robbins-Monro [57] algorithm.
The usual hypothesis on the adaptation parameter to get almost sure results is
then:
εt = +∞ and
ε2t < +∞. (5)
The less restrictive conditions
t εt = +∞ and εt ց 0 generally do not ensure the
almost sure convergence, but some weaker convergence, for instance the convergence
in probability.
Let us first examine the results in dimension 1.
3 The dimension 1
3.1 The self-organization
The input space is [0, 1], the dimension d is 1 and the units are arranged on a linear
array. The neighborhood function Λ is supposed to be non increasing as a function
of the distance between units, the classical step neighborhood function satisfies this
condition. The input distribution µ is continuous on [0, 1]: this means that it does
not weight any point. This is satisfied for example by any distribution having a
density.
Let us define
F+n = {m ∈ R / 0 < m1 < m2 < . . . < mn < 1}
F−n = {m ∈ R / 0 < mn < mn−1 < . . . < m1 < 1}.
In [9], [6], the following results are proved using Markovian methods :
Theorem 1 (i) The two sets F+n and F
n are absorbing sets.
(ii) If ε is constant, and if Λ is decreasing as a function of the distance (e.g. if there
are only two neigbors) the entering time τ , that is the hitting time of F+n ∪ F
n , is
almost surely finite, and ∃λ > 0, s.t. supm∈[0,1]n Em(exp(λτ)) is finite, where Em
denote the expectation given m(0) = m.
The theorem 1 ensures that the algorithm will almost surely order the weights.
These results can be found for the more particular case (µ uniform and two neigh-
bors) in Cottrell and Fort [9], 1987, and the succesive generalisations in Erwin et
al. [21], 1992, Bouton and Pagès [6], 1993, Fort and Pagès [27], 1995, Flanagan [23],
1996.
The techniques are the Markov chain tools.
Actually following [6], it is possible to prove that whenever ε ց 0 and
εt = +∞,
then ∀m ∈ [0, 1]n,Probam(τ < +∞) > 0, (that is the probability of self-organization
is positive regardless the initial values, but not a priori equal to 1). In [60], Sadeghi
uses a generalized definition of the winner unit and shows that the probability of
self-organization is uniformly positive, without assuming a lower bound for εt.
No result of almost sure reordering with a vanishing εt is known so far. In [10], Cot-
trell and Fort propose a still not proved conjecture: it seems that the re-organization
occurs when the parameter εt has a
order.
3.2 The convergence for dimension 1
After having proved that the process enters an ordered state set (increasing or
decreasing), with probability 1, it is possible to study the convergence of the process.
So we assume that m(0) ∈ F+n . It would be the same if m(0) ∈ F
3.2.1 Decreasing adaptation parameter
In [9] (for the uniform distribution), in [7], [27] and more recently in [3], [4], 1997,
the almost sure convergence is proved in a very general setting. The results are
gathered in the theorem below :
Theorem 2 Assume that
1) (εt) ∈]0, 1[ satisfies the condition (5),
2) the neighborhood function satisfies the condition HΛ: there exists k0 <
that Λ(k0 + 1) < Λ(k0),
3) the input distribution µ satisfy the condition Hµ: it has a density f such that
f > 0 on ]0, 1[ and ln(f) is strictly concave (or only concave, with lim0+ f + lim1− f
positive),
(i) The mean function h has a unique zero m⋆ in F+n .
(ii) The dynamical system dm
= −h(m) is cooperative on F+n , i.e. the non diagonal
elements of ∇h(m) are non positive.
(iii) m⋆ is attracting.
So if m(0) ∈ F+n , m(t)
−→ m⋆ almost surely.
In this part, the authors use the ODE method, a result by M.Hirsch on cooperative
dynamical system [34], and the Kushner & Clark Theorem [41], [3]. A.Sadeghi put
in light that the non-positivity of non-diagonal terms of ∇h is exactly the basic
definition of a cooperative dynamical system and he obtained partial results in [59]
and more general ones in [60].
We can see that the assumptions are very general. Most of the usual probability
distributions (truncated on [0, 1]) have a density f such that ln(f) is strictly concave.
On the other hand, the uniform distribution is not strictly ln-concave as well as the
truncated exponential distribution, but both cumply the condition lim0+ f +lim1− f
positive.
Condition (5) is essential, because if εt ց 0 and
t εt = +∞, there is only a
priori convergence in probability.
In fact, by studying the associated ODE, Flanagan [22] shows that before ordering,
it can appear metastable equilibria.
In the uniform case, it is possible to calculate the limit m⋆. Its coordinates are
solutions of a (n × n)-linear system which can be found in [37] or [9]. An explicit
expression, up to the solution of a 3 × 3 linear system is proposed in [6]. Some
further investigations are made in [31].
3.2.2 Constant adaptation parameter
Another point of view is to study the convergence of m(t) when εt = ε is a constant.
Some results are available when the neighborhood function corresponds to the two-
neighbors setting. See [9], 1987, (for the uniform distribution) and [7], 1994, for the
more general case. One part of the results also hold for a more general neighborhood
function, see [3], [4].
Theorem 3 Assume that m(0) ∈ F+n ,
Part A: Assume that the hypotheses Hµ and HΛ hold as in Theorem 2, then
For each ε ∈]0, 1[, there exists some invariant probability νε on F+n .
Part B: Assume only that Λ(i, j) = 1 if and only if |i − j| = 0 or 1 (classical
2-neighbors setting),
(i) If the input distribution µ has an absolutely continuous part (e.g. has a density),
then for each ε ∈]0, 1[, there exists a unique probability distribution νε such that the
distribution of mt weakly converges to νε when t −→ ∞. The rate of convergence is
geometric. Actually the Markov chain is Doeblin recurrent.
(ii) Furthermore, if µ has a positive density, ∀ε, νε is equivalent to the Lebesgue
measure on F+n if and only if n is congruent with 0 or 1 modulo 3. If n is congruent
with 2 modulo 3, the Lebesgue measure is absolutely continuous with respect to νε ,
but the inverse is not true, that is νε has a singular part.
Part C: With the general hypotheses of Part A (which includes that of Part B), if
m⋆ is the unique globally attractive equilibrium of the ODE (see Theorem 2), thus
νε converges to the Dirac distribution on m⋆ when ε ց 0 .
So when ε is very small, the values will remain very close to m⋆.
Moreover, from this result we may conjecture that for a suitable choice of εt,
certainly εt =
, where A is a constant, both self-organization and convergence
towards the unique m⋆ can be achieved. This could be proved by techniques very
similar to the simulated annealing methods.
4 The 0 neighbor case in a multidimensional set-
In this case, we take any dimension d, the input space is Ω ⊂ Rd and Λ(i, j) = 1 if
i = j, and 0 elsewhere. There is no more topology on I, and reordering no makes
sense. In this case the algorithm is essentially a stochastic version of the Linde, Gray
and Buzo [44] algorithm (LBG). It belongs to the family of the vectorial quantization
algorithms and is equivalent to the Competitive Learning. The mathematical results
are more or less reachable. Even if this algorithm is deeply different from the usual
Kohonen algorithm, it is however interesting to study it because it can be viewed
as a limit situation when the neighborhood size decreases to 0.
The first result (which is classical for Competitive learning), and can be found in
[54], [50], [39] is:
Theorem 4 (i) The 0-neighbor algorithm derives from the potential
Vn(m) =
1≤i≤n
‖mi − x‖
2dµ(x) (6)
(ii) If the distribution probability µ is continuous (for example µ has a density f),
Vn(m) =
Ci(m)
‖mi − x‖
2f(x)dx =
1≤i≤n
‖mi − x‖
2f(x)dx (7)
where Ci(m) is the Voronöı set related with the unit i for the current state m.
The potential function Vn(m) is nothing else than the intra-classes variance used
by the statisticians to characterize the quality of a clustering. In the vectorial quan-
tization setting, Vn(m) is called distortion. It is a measure of the loss of information
when replacing each input by the closest weight vector (or code vector). The po-
tential Vn(m) has been extensively studied since 50 years, as it can be seen in the
Special Issue of IEEE Transactions on Information Theory (1982), [42].
The expression (7) holds as soon as mi 6= mj for all i 6= j and as the borders of
the Voronöı classes have probability 0, (µ(∪ni=1∂Ci(m)) = 0). This last condition is
always verified when the distribution µ has a density f . With these two conditions,
V (m) is differentiable at m and its gradient vector reads
∇Vn(m) =
Ci(m)
(mi − x)f(m)d(m)
So it becomes clear ([50],[40]) that the Kohonen algorithm with 0 neighbor is the
stochastic gradient descent relative to the function Vn(m) and can be written :
m(t + 1) = m(t)− εt+11Ci(m(t))(x(t+ 1))(m(t)− x(t + 1))
where 1Ci(m(t))(x(t + 1)) is equal to 1 if x(t+ 1) ∈ Ci(m(t)), and 0 if not.
The available results are more or less classical, and can be found in [44] and [8],
for a general dimension d and a distribution µ satisfying the previous conditions.
Concerning the convergence results, we have the following when the dimension
d = 1, see Pagès ([50], [51]), the Special Issue in IEEE [42] and also [43] for (ii):
The parameter ε(t) has to satisfy the conditions (5).
Theorem 5 Quantization in dimension 1
(i) If ∇Vn has finitely many zeros in F
n , m(t) converges almost surely to one of
these local minima.
(ii) If the hypothesis Hµ holds (see Theorem (2)), Vn has only one zero point in
F+n , say m
n. This point m
n ∈ F
n and is a minimum. Furthermore if m(0) ∈ F
−→ m⋆n.
(iii) If the stimuli are uniformly distributed on [0, 1], then
m⋆n = ((2i− 1)/2n)1≤i≤n.
The part (ii) shows that the global minimum de Vn(m) is reachable in the one-
dimensional case and the part (iii) is a confirmation of the fact that the algorithm
provides an optimal discretization of continous distributions.
A weaker result holds in the d-dimensional case, because one has only the conver-
gence to a local minimum of Vn(m).
Theorem 6 Quantization in dimension d
If ∇Vn has finitely many zeros in F
n , and if these zeros have all their components
pairwise distinct, m(t) converges almost surely to one of these local minima.
In the d-dimensional case, we are not able to compute the limit, even in the
uniform case. Following [48] and many experimental results, it seems that the
minimum distortion could be reached for an hexagonal tesselation, as mentioned in
[31] or [40].
In both cases, we can set the properties of the global minima of Vn(m), in the
general d-dimensional setting. Let us note first that Vn(m) is invariant under any
permutation of the integers 1, 2, . . . , n. So we can consider one of the global minima,
the ordered one (for example the lexicographically ordered one).
Theorem 7 Quantization property
(i) The function Vn(m) is continuous on (R
d)n and reaches its (global) minima
inside Ωn.
(ii) For a fixed n, a point m⋆n at which the function Vn is minimum has pairwise
distinct components.
(iii) Let n be a variable and m⋆n = (m
n,1, m
n,2, . . . , m
n,n) the ordered minimum of
Vn(m). The sequence min(Rd)n Vn(m) = Vn(m
n) converges to 0 as n goes to +∞.
More precisely, there exists a speed β = 2/d and a constante A(f) such that
nβVn(m
n) −→ A(f)
when n goes to +∞.
Following Zador [64], the constant A(f) can be computed, A(f) = ad ‖ f ‖ρ,
where ad does not depend on f , ρ = d/(d+ 2) and ‖ f ‖ρ= [
f ρ(x)dx]1/ρ.
(iv) Then, the weighted empirical discrete probability measure
µ(Ci(m
n))δm⋆n,i
converges in distribution to the probability measure µ, when n → ∞.
(v) If Fn (resp. F ) denotes the distribution function of µn (resp. µ), one has
(Rd)n
Vn(m) = min
(Rd)n
(Fn(x)− F (x))
so when n → ∞, Fn converges to F in quadratic norm.
The convergence in (iv) properly defines the quantization property, and explains
how to reconstruct the input distribution from the n code vectors after convergence.
But in fact this convergence holds for any sequence y⋆n = y1,n, y2,n, . . . , yn,n, which
“fills ” the space when n goes to +∞: for example it is sufficient that for any n,
there exists an integer n′ > n such that in any interval yi,n, yi+1,n (in R
d), there are
some points of y⋆n′. But for any sequence of quantizers satisfying this condition, even
if there is convergence in distribution, even if the speed of the convergence can be
the same, the constant A(f) will differ since it will not realize the minimum of the
distortion.
For each integer n, the solution m⋆n which minimizes the quadratic distortion
Vn(m) and the quadratic norm ‖ Fn − F ‖
2 is said to be an optimal n-quantizer
. It ensures also that the discrete distribution function associated to the minimum
m⋆n suitably weighted by the probability of the Voronöı classes, converges to the
initial distribution function F . So the 0-neighbor algorithm provides a skeleton of
the input distribution and as the distortion tends to 0 as well as the quadratic norm
distance of Fn and F , it provides an optimal quantizer. The weighting of the Dirac
functions by the volume of the Voronöı classes implies that the distribution µn is
usually quite different from the empirical one, in which each term would have the
same weight 1/n.
This result has been used by Pagès in [50] and [51] to numerically compute inte-
grals. He shows that the speed of convergence of the approximate integrals is exactly
d for smooth enough functions, which is faster than the Monte Carlo method while
d ≤ 4.
The difficulty remains that the optimal quantizer m⋆n is not easily reachable, since
the stochastic process m(t) converges only to a local minimum of the distortion,
when the dimension is greater than 1.
Magnification factor
There is some confusion [37], [52], between the asymptotic distribution of an
optimal quantizer m⋆n when n −→ ∞ and that one of the best random quantizer, as
defined by Zador [64] in 1982.
The Zador’s result, extended to the multi-dimensional case, is as follows : Let
f be the input density of the measure µ, and (Y1, Y2, . . . , Yn) a random quantizer,
where the code vectors Yi are independent with common distribution of density g.
Then, with some weak assumptions about f and g, the distortion tends to 0 when
n −→ ∞, with speed β = 2/d, and it is possible to define the quantity
A(f, g) = lim
nβEg[
‖Yi − x‖
2f(x)dx]
Then for any given input density f , the density g (assuming some weak condition)
which minimises A(f, g) is
g⋆ ∼ C f d/d+2.
The inverse of the exponent d/(d + 2) is refered as Magnification Factor. Note
that in any case, when the data dimension is large, this exponent is near 1 (it value
is 1/3 when d = 1). Note also that this power has no effect when the density f is
uniform. But in fact the optimal quantizer is another thing, with another definition.
Namely the optimal quantizerm⋆n (formed with the code vectorsm
1,n, m
2,n, . . . , m
n,n),
minimizes the distortion Vn(m), and is got after convergence of the 0-neighbor al-
gorithm (if we could ensure the convergence to a global minimum, that is true only
in the one-dimensional case). So if we set
An(f,m
n) = n
βVn(m
n) = n
‖m⋆i,n − x‖
2f(x)dx
actually we have,
A(f) = lim
An(f,m
n) < A(f, g
and the limit of the discrete distribution of m⋆n is not equal to g
⋆. So there is no
magnification factor, for the 0-neighbor algorithm as claimed in many papers. It can
be an approximation, but no more.
The problem comes from the confusion between two distinct notions: random
quantizer and optimal quantizer. And in fact, the good property is the convergence
of the weighted distribution function (7).
As to the SOM algorithm in the one-dimensional case, with a neighborhood func-
tion not reduced to the 0-neighbor case, one can find in [55] or [19] some result
about a possible limit of the discrete distribution when the number of units goes to
∞. But actually, the authors use the Zador’s result which is not appropriate as we
just see.
5 The multidimensional continuous setting
In this section, we consider a general neighborhood function and the SOM algorithm
is defined as in Section 2.
5.1 Self-organization
When the dimension d is greater than 1, little is known on the classical Kohonen
algorithm. The main reason seems to be the fact that it is difficult to define what can
be an organized state and that no absorbing sets have been found. The configurations
whose coordinates are monotoneous are not stable, contrary to the intuition. For
each configuration set which have been claimed to be left stable by the Kohonen
algorithm, it has been proved later that it was possible to go out with a positive
probability. See for example [10]. Most people think that the Kohonen algorithm
in dimension greater than 1 could correspond to an irreducible Markov chain, that
is a chain for which there exists always a path with positive probability to go from
anywhere to everywhere. That property imply that there is no absorbing set at all.
Actually, as soon as d ≥ 2, for a constant parameter ε, the 0-neighbor algorithm
is an Doeblin recurrent irreducible chain (see [7]), that cannot have any absorbing
class.
Recently, two apparently contradictory results were established, that can be col-
lected together as follows.
Theorem 8 (d = 2 and ε is a constant) Let us consider a n × n units square
network and the set F++ of states whose both coordinates are separately increasing
as function of their indices, i.e.
F++ =
∀i1 ≤ n,m
< m2i1,2 < . . . < m
, ∀i2 ≤ n,m
< m12,i2 < . . . < m
(i) If µ has a density on Ω, and if the neighborhood function Λ is everywhere posi-
tive and decreases with the distance, the hitting time of F++ is finite with positive
probability (i.e. > 0, but possibly less than 1). See Flanagan ([22], [23]).
(ii) In the 8-neighbor setting, the exit time from F++ is finite with positive proba-
bility. See Fort and Pagès in ([28]).
This means that (with a constant, even very small, parameter ε), the organi-
zation is temporarily reached and that even if we guess that it is almost stable,
dis-organization may occur with positive probability.
More generally, the question is how to define an organized state. Many authors
have proposed definitions and measures of the self-organization, [65], [18], [62], [32],
[63], [33]. But none such “organized” sets have a chance to be absorbing.
In [28], the authors propose to consider that a map is organized if and only if the
Voronöı classes of the closest neighboring units are contacting. They also precisely
define the nature of the organization (strong or weak).
They propose the following definitions :
Definition 1 Strong organization
There is strong organization if there exists a set of organized states S such that
(i) S is an absorbing class of the Markov chain m(t),
(ii) The entering time in S is almost surely finite, starting from any random weight
vectors (see [6]).
Definition 2 Weak organization
There is weak organization if there exists a set of organized states S such that all
the possible attracting equilibrium points of the ODE defined in 3 belong to the set
The authors prove that there is no strong organization at least in two seminal
cases: the input space is [0, 1]2, the network is one-dimensional with two neighbors or
two-dimensional with eight neighbors. The existence of weak organization should be
investigated as well, but until now no exact result is available even if the simulations
show a stable organized limit behavior of the SOM algorithm.
5.2 Convergence
In [27], (see also [26]) the gradient of h is computed in the d-dimensional setting
(when it exists). In [53], the convergence and the nature of the limit state is studied,
assuming that the organization has occured, although there is no mathematical proof
of the convergence.
Another interesting result received a mathematical proof thanks to the computa-
tion of the gradient of h: it is the dimension selection effect discovered by Ritter
and Schulten (see [53]). The mathematical result is (see [27]:
Theorem 9 Assume thatm⋆1 is a stable equilibrium point of a general d1-dimensional
Kohonen algorithm, with n1 units, stimuli distribution µ1 and some neighborhood
function Λ. Let µ2 be a d2-dimensional distribution with mean m
2 and covariance
matrix Σ2. Consider the d1 + d2 Kohonen algorithm with the same units and the
same neighborhood function. The stimuli distribution is now µ1
Then there exists some η > 0, such that if ‖Σ2‖ < η, the state m
1 in the subspace
m2 = m
2 is still a stable equilibrium point for the d1 + d2 algorithm.
It means that if the stimuli distribution is close to a d1-dimensional distribution in
the d1 + d2 space, the algorithm can find a d1-space stable equilibrium point. That
is the dimension selection effect.
From the computation of the gradient ∇h, some partial results on the stability of
grid equilibriums can also be proved:
Let us consider I = I1×I2×. . .×Id a d-dimensional array, with Il = {1, 2, . . . , nl},
for 1 ≤ l ≤ d. Let us assume that the neighborhood function is a product function
(for example 8 neighbors for d = 2) and that the input distributions in each coordi-
nate are independent, that is µ = µ1
. . .
µd. At last suppose that the support
of each µl is [0,1].
Let us call grid states the states m⋆ = (m⋆ill, 1 ≤ il ≤ nl, 1 ≤ l ≤ d), such that
for every 1 ≤ l ≤ d, (m⋆ill, 1 ≤ il ≤ nl) is an equilibrium for the one-dimensional
algorithm. Then the following results hold [27] :
Theorem 10 (i) The grid states are equilibrium points of the ODE (3) in the d-
dimensional case.
(ii) For d = 2, if µ1 and µ2 have strictly positive densities f1 and f2 on [0, 1], if the
neighborhood functions are strictly decreasing, the grid equilibrium points are not
stable as soon as n1 is large enough and the ratio
is large (or small) enough (i.e.
when n1 −→ +∞ and
−→ +∞ or 0, see [27], Section 4.3).
(iii) For d = 2, if µ1 and µ2 have strictly positive densities f1 and f2 on [0, 1], if
the neighborhood functions are degenerated (0 neighbor case), m⋆ is stable if n1 and
n2 are less or equal to 2, is not stable in any other case (may be excepted when
n1 = n2 = 3).
The (ii) gives a negative property for the non square grid which can be related
with this one: the product of one-dimensional quantizers is not the correct vectorial
quantization. But also notice that we have no result about the simplest case: the
square grid equilibrium in the uniformly distributed case. Everybody can observe by
simulation that this square grid is stable (and probably the unique stable “organized”
state). Nevertheless, even if we can numerically verify that it is stable, using the
gradient formula it is not mathematically proved even with two neighbors in each
dimension!
Moreover, if the distribution µ1 and µ2 are not uniform, generally the square grids
are not stables, as it can be seen experimentally.
6 The discrete case
In this case, there is a finite number N of inputs and Ω = {x1, x2, . . . , xN}. The
input distribution is uniform on Ω that is µ(dx) = 1
l=1 δxl. It is the setting of
many practical applications, like Classification or Data Analysis.
6.1 The results
The main result ([39], [56]) is that for not time-dependent general neighborhood,
the algorithm locally derives from the potential
Vn(m) =
xl∈Ci(m)
Λ(i− j)‖mj − xl‖
Ci(m)
Λ(i− j)‖mj − x‖
2)µ(dx)
i,j=1
Λ(i− j)
Ci(m)
‖mj − x‖
2µ(dx).
When Λ(i, j) = 1 if i and j are neighbors, and if V(j) denotes the neighborhood
of unit i in I, Vn(m) also reads
Vn(m) =
∪i∈V(j)Ci(m)
‖mj − x‖
2µ(dx).
Vn(m) is an intra-class variance extended to the neighbor classes which is a gen-
eralization of the distortion defined in Section 4 for the 0-neighbor setting. But this
potential does have many singularities and its complete analysis is not achieved,
even if the discrete algorithm can be viewed as a stochastic gradient descent proce-
dure. In fact, there is a problem with the borders of the Voronöı classes. The set of
all these borders along the process m(t) trajectories has measure 0, but it is difficult
to assume that the given points xl never belong to this set.
Actually the potential is the true measure of the self-organization. It measures
both clustering quality and proximity between classes. Its study should provide
some light on the Kohonen algorithm even in the continuous case.
When the stimuli distribution is continuous, we know that the algorithm is not a
gradient descent [21]. However the algorithm can be seen then as an approximation
of the stochastic gradient algorithm derived from the function Vn(m). Namely,
the gradient of Vn(m) has a non singular part which corresponds to the Kohonen
algorithm and a singular one which prevents the algorithm to be a gradient descent.
This remark is the base of many applications of the SOM algorithm as well in
combinatorial optimization, data analysis, classification, analysis of the relations
between qualitative classifying variables.
6.2 The applications
For example, in [24], Fort uses the SOM algorithm with a close one-dimensional
string, in a two dimensional space where are located M cities. He gets very quickly
a very good sub-optimal solution. See also the paper [1].
The applications in data analysis and classification are more classical. The prin-
ciple is very simple: after convergence, the SOM algorithm provides a two(or one)-
dimensional organized classification which permit a low dimensional representation
of the data. See in [40] an impressive list of examples.
In [15] and [17], an application to forecasting is presented from a previous classi-
fication by a SOM algorithm.
6.3 Analysis of qualitative variables
Let us define here two original algorithms to analyse the relations between qualitative
variables. The first one is defined only for two qualitative variables. It is called
KORRESP and is analogous to the simple classical Correspondence Analysis. The
second one is devoted to the analysis of any finite number of qualitative variables.
It is called KACM and is similar to the Multiple Correspondence Analysis. See [11],
[14], [16] for some applications.
For both algorithms, we consider a sample of individuals and a number K of
questions. Each question k, k = 1, 2, . . . , K has mk possible answers (or modalities).
Each individual answers each question by choosing one and only one modality. If
1≤k≤mk is the total number of modalities, each individual is represented by
a row M-vector with values in 0, 1. There is only one 1 between the 1st component
and the m1-th one, only one 1 between the m1+1-th component and the m1+m2-th
one and so on.
In the general case whereM > 2, the data are summarized into a Burt Table which
is a cross tabulation table. It is a M × M symmetric matrix and is composed of
K×K blocks, such that the (k, l)-block Bkl (for k 6= l) is the (mk×ml) contingency
table which crosses the question k and the question l. The block Bkk is a diagonal
matrix, whose diagonal entries are the numbers of individuals who have respectively
chosen the modalities 1, 2, . . . , mk for question k. In the following, the Burt Table
is denoted by B.
In the case M = 2, we only need the contingency table T which crosses the two
variables. In that case, we set p (resp. q) for m1 (resp. m2).
The KORRESP algorithm
In the contingency table T , the first qualitative variable has p levels and corre-
sponds with the rows. The second one has q levels and corresponds with the columns.
The entry nij is the number of individuals categorized by the row i and the column j.
From the contingency table, the matrix of relative frequencies (fij = nij/(
ij nij))
is computed.
Then the rows and the columns are normalized in order to have a sum equal to
1. The row profile r(i), 1 ≤ i ≤ p is the discrete probability distribution of the
second variable given that the first variable has modality i and the column profile
c(j), 1 ≤ j ≤ q is the discrete probability distribution of the first variable given
that the second variable has modality j. The classical Correspondence Analysis is a
simultaneous weighted Principal Component Analysis on the row profiles and on the
column profiles. The distance is chosen to be the χ2 distance. In the simultaneous
representation, related modalities are projected into neighboring points.
To define the algorithm KORRESP, we build a new data matrix D : to each row
profile r(i), we associate the column profile c(j(i)) which maximizes the probability
of j given i, and conversely, we associate to each column profile c(j) the row profile
r(i(j)) the most probable given j. The data matrix D is the ((p + q) × (q + p))-
matrix whose first p rows are the vectors (r(i), c(j(i))) and last q rows are the vectors
(r(i(j)), c(j)). The SOM algorithm is processed on the rows of this data matrix D.
Note that we use the χ2 distance to look for the winning unit and that we alterna-
tively pick at random the inputs among the p first rows and the q last ones. After
convergence, each modality of both variables is classified into a Voronöı class. Re-
lated modalities are classified into the same class or into neighboring classes. This
method give a very quick, efficient way to analyse the relations between two quali-
tative variables. See [11] and [12] for real-world applications.
The KACM Algorithm
When there are more than two qualitative variables, the above method does not
work any more. In that case, the data matrix is just the Burt Table B. The rows are
normalized, in order to have a sum equal to 1. At each step, we pick a normalized
row at random according to the frequency of the corresponding modality. We define
the winning unit according to the χ2 distance and update the weights vectors as
usual. After convergence, we get an organized classification of all the modalities,
where related modalities belong to the same class or to neighboring classes. In that
case also, the KACM method provides a very interesting alternative to classical
Multiple Correspondence Analysis.
The main advantages of both KORRESP and KACM methods are their rapidity
and their small computing time. While the classical methods have to use several
representations with decreasing information in each, ours provide only one map,
that is rough but unique and permit a rapid and complete interpretation. See [14]
and [16] for the details and financial applications.
7 Conclusion
So far, the theoretical study in the one-dimensional case is nearly complete. It
remains to find the convenient decreasing rate to ensure the ordering. For the
multidimensional setting, the problem is difficult. It seems that the Markov chain
is irreducible and that further results could come from the careful study of the
Ordinary Differential Equation (ODE) and from the powerful existing results about
the cooperative dynamical systems.
On the other hand, the applications are more and more numerous, especially
in data analysis, where the representation capability of the organized data is very
valuable. The related methods make up a large and useful set of methods which
can be substituted to the classical ones. To increase their use in the statistical
community, it would be necessary to continue the theoretical study, in order to
provide quality criteria and performance indices with the same rigour as for the
classical methods.
Acknowledgements
We would like to thank the anonymous rewiewers for their helpful comments.
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	Introduction
	Notations and definitions
	The dimension 1
	The self-organization
	The convergence for dimension 1
	Decreasing adaptation parameter
	Constant adaptation parameter
	The 0 neighbor case in a multidimensional setting
	The multidimensional continuous setting
	Self-organization
	Convergence
	The discrete case
	The results
	The applications
	Analysis of qualitative variables
	Conclusion
