TITLE: Rank Based Pseudoinverse Computation in Extreme Learning Machine for Large Datasets 

ABSTRACT: Extreme Learning Machine (ELM) is an efficient and effective
least-square-based learning algorithm for classification, regression problems
based on single hidden layer feed-forward neural network (SLFN). It has been
shown in the literature that it has faster convergence and good generalization
ability for moderate datasets. But, there is great deal of challenge involved
in computing the pseudoinverse when there are large numbers of hidden nodes or
for large number of instances to train complex pattern recognition problems. To
address this problem, a few approaches such as EM-ELM, DF-ELM have been
proposed in the literature. In this paper, a new rank-based matrix
decomposition of the hidden layer matrix is introduced to have the optimal
training time and reduce the computational complexity for a large number of
hidden nodes in the hidden layer. The results show that it has constant
training time which is closer towards the minimal training time and very far
from worst-case training time of the DF-ELM algorithm that has been shown
efficient in the recent literature.
Extreme Learning Machine (ELM) is an efficient and effective
least-square-based learning algorithm for classification, regression problems
based on single hidden layer feed-forward neural network (SLFN). It has been
shown in the literature that it has faster convergence and good generalization
ability for moderate datasets. But, there is great deal of challenge involved
in computing the pseudoinverse when there are large numbers of hidden nodes or
for large number of instances to train complex pattern recognition problems. To
address this problem, a few approaches such as EM-ELM, DF-ELM have been
proposed in the literature. In this paper, a new rank-based matrix
decomposition of the hidden layer matrix is introduced to have the optimal
training time and reduce the computational complexity for a large number of
hidden nodes in the hidden layer. The results show that it has constant
training time which is closer towards the minimal training time and very far
from worst-case training time of the DF-ELM algorithm that has been shown
efficient in the recent literature.
Rank Based Pseudoinverse Computation in Extreme Learning Machine for Large Datasets
http://arxiv.org/abs/2011.02436v1