matlab源代码，是非负矩阵分解工具箱，包含乘法更新，最小二乘约束，投影梯度法等方法-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