I have a complex square matrix, and wish to learn latent factors (equally weighted latent factors, so not SVD) from this matrix.
Given a Hermitian matrix
A of dimensions n x n, how can I find a matrix
V of dimensions n x k such that
A as closely as possible?
I tried Bayesian MCMC factorization using the CoGAPS algorithm, but surprisingly the resulting
V matrices were nowhere near identical, and as a consequence the results were confusing.
I have also tried SVD, but the problem here is that individual latent factors in the input matrix are often combined at high singular values.
To be clear, this is a real world problem I am solving and I'm failing to find any precedent. I appreciate any suggestions. I am doing my analysis in R, but am looking for the most computationally reasonable algorithm as this will be done on a 25000x25000 non-sparse matrix.