Do there exist generalized determinants, and if so, in which senses? It is well known that a determinant of a matrix is the product of eigenvalues:
$$\det({\bf A}) = \prod_{\forall i}\lambda_i({\bf A})$$
and product of singular values equal to the norm of determinant as @Batominovski pointed out:
$$|\det({\bf A})| = \prod_{\forall i}\sigma_i({\bf A})$$
This has useful interpretations for many kinds of matrices in many applications. Now if we look at the $\bf M_2$ matrices from this answer, we see that we can have a matrix that performs "summing" of independent sub-spaces but will always have determinant 1. Could we generalize our notion of determinant to somehow measure this summing ability? If we are aware of the $2\times 2$ block structure we could "pretend" that each such block along the diagonal was an eigenvalue, and some kind of generalized determinant would then be the product of them which would yield a sum for those off-diagonal position, like this:
$$\text{bdet}({\bf M_2}(6)) = \left[\begin{array}{cc}1&0\\1&1\end{array}\right]\left[\begin{array}{cc}1&0\\1&1\end{array}\right]\left[\begin{array}{cc}1&0\\0&1\end{array}\right]=\left[\begin{array}{cc}1&0\\2&1\end{array}\right]$$
Where the two is the sum of prime exponents. b could stand for block, or something.
Does this seem useful or make any sense or am I just rambling about?

If we require, that the generalized determinant $gdet$ is equivalent to the standard determinant for square matrices, then we cannot expect, that 
$gdet(AB) = gdet(A)\times gdet(B)$ for any $A$, $B$, unless $gdet(A) \equiv 0$ for all non square matrices $A$. 
To see this it is enough to consider a diagonal square matrix $A$ and a matrix $B$ being $i$-th column of the identity matrix.
Without this property I don't think, that such generalization of the determinant could be useful.
