Introduction - If you have any usage issues, please Google them yourself
Usage:
[W,H] = nmf(X,K,alg,maxiter,speak)
W: output matrix
H: output matrix
X: input matrix
K: number of components
alg: algorithm to use
maxiter: maximum number of iterations
speak: print to screen
Algorithms:
mm: Multiplicative updates method using euclidean distance measure.
cjlin: Projected gradient method
prob: Probabilistic non-negative matrix factorization.
als: Alternating least squares.
alsobs: Alternating least squares with optimal brain surgeon.
Demonstrations:
PET: NMF on a PET dataset
Text: NMF used on a three different datasets Email, medical, and CNN.
Algorithms
mm: Multiplicative update method using euclidean distance measure.
Described in Lee and Seung, 2001, Algorithms for Non-negative Matrix Factorization, Advances in Neural Information Processing Systems 13, 556-562. This algorithm is the most commonly used algorithm to solve NMF.
cjlin: Alternative non-negative least squares using projected gradients.
Author: Chih-Je