EMPCA

Expectation-Maximization Principal Component Analysis

EMPCA calculates principal components using an expectation maximization algorithm to find each component in the residual matrix after substracting the previously converged principal components.
EMPCA_W accepts a weight matrix to use in the weighted EM algorithm.
EMPCA_NAN accepts a data matrix with nans to use in the missing data EM algorithm.
An informative message reports the number of EM iterations computed for each component, revealing if the convergence was achieved under a certain tolerance, or if the iterations were stopped after a maximum number.
This implementation is especially useful to handle large matrices, and runs fast on gpuArray matrices.
The algorithm is described in
Bailey, Stephen. "Principal Component Analysis with Noisy and/or Missing Data." Publications of the Astronomical Society of the Pacific 124.919 (2012): 1015-1023.
http://arxiv.org/pdf/1208.4122v2.pdf