文件列表:

LICENSE (1513, 2016-10-27)
NMFL0_H.m (12710, 2016-10-27)
NMFL0_W.m (5873, 2016-10-27)
NMP.m (1835, 2016-10-27)
NNBP.m (1428, 2016-10-27)
NNBP_Matlab_Opt.m (2423, 2016-10-27)
NNLS_PG.m (2903, 2016-10-27)
NNLS_PG_mask.m (3259, 2016-10-27)
combinatorialLS.m (1321, 2016-10-27)
concatImg.m (513, 2016-10-27)
createDictionaryRand.m (328, 2016-10-27)
example_NMFL0_H.m (974, 2016-10-27)
example_ORL_NMFL0_W.m (1389, 2016-10-27)
example_sNNLS_rsNNLS.m (1258, 2016-10-27)
experiment_NMFL0_H_spectrogram.m (4994, 2016-10-27)
experiment_NMFL0_W_ORLFaces.m (6767, 2016-10-27)
experiment_SparseCoder_SyntheticData.m (10461, 2016-10-27)
hoyerS.m (385, 2016-10-27)
maskNNLS.m (4105, 2016-10-27)
rsNNLS___.m (744, 2016-10-27)
sNNLS___.m (726, 2016-10-27)
sparseNNLS.m (7157, 2016-10-27)

# l0-sparse-NMF This package reproduces results from Robert Peharz and Franz Pernkopf, "Sparse Nonnegative Matrix Factorization with l0-constraints", Neurocomputing, vol. 80, pp. 38--46, 2012. In particular, it provides algorithms for approximate non-negative matrix factorization with l0-sparseness constraints. PLEASE NOTE THE ACCOMPANYING LICENSE FILE (modified BSD, 3-Clause). IF YOU USE THIS CODE FOR RESEARCH, PLEASE CITE THE PAPER ABOVE. Overview: NMFL0_H.m: implements approximate NMF with l0-sparseness constraints on the columns of H. See help text in m-file for further information. NMFL0_W.m: implements approximate NMF with l0-sparseness constraints on the columns of W. See help text in m-file for further information. sparseNNLS.m: implements several functions, such as nonnegative least squares (NNLS), sparse nonnegative least squares (sNNLS) and reverse sparse nonnegative least squares (rsNNLS). See help text in m-file for further information. experiment_SparseCoder_SyntheticData.m: reproduces experiment in section 4.1 experiment_NMFL0_H_spectrogram.m: reproduces experiment in section 4.2 experiment_NMFL0_W_ORLFaces.m: reproduces experiment in section 4.3 example_*.m: shorter application examples

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