MPCA is a comprehensive suite of tools for doing
discrete principal components analysis on data
sets of size 100Mb or more. Scaling is done using
sparse vectors, multi-threading, memory mapping,
and other POSIX tricks. Reports, file dumping
utilities, and other utilities are included. The
general problem of discrete components analysis is
variously called grade of membership, PLSA,
non-neg.matrix factorization, multinomial
admixtures, LDA, and multinomial PCA.
