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- matlab
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[Matlab]
[源码]
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- 15.02mb
- Update:
- 2015-06-26
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- 沈猴子
Description: 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
To Search:
File list (Check if you may need any files):
NMF-DTU-Toolbox
...............\IndianPines.mat
...............\READ ME.txt
...............\compare.m
...............\email.zip
...............\nmf.m
...............\nmf_als.asv
...............\nmf_als.m
...............\nmf_alsobs.m
...............\nmf_cjlin.m
...............\nmf_euclidean_dist.m
...............\nmf_mm.m
...............\nmf_prob.m
...............\order_comp.m
...............\petAnalyzeImage.zip
...............\sina_live_setup20130528.exe
...............\test_toolbox.m
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