
 Description:
Nimfa is an opensource Python library that provides a unified interface to nonnegative matrix factorization algorithms. It includes implementations of stateoftheart factorization methods, initialization approaches, and quality scoring. Both dense and sparse matrix representation are supported.
Matrix Factorization Methods
 BD  Bayesian nonnegative matrix factorization Gibbs sampler
 BMF  Binary matrix factorization
 ICM  Iterated conditional modes nonnegative matrix factorization
 LFNMF  Fisher nonnegative matrix factorization for learning local features
 LSNMF  Alternating nonnegative least squares matrix factorization using projected gradient method for subproblems
 NMF  Standard nonnegative matrix factorization with Euclidean / KullbackLeibler update equations and Frobenius / divergence / connectivity cost functions
 NSNMF  Nonsmooth nonnegative matrix factorization
 PMF  Probabilistic nonnegative matrix factorization
 PSMF  Probabilistic sparse matrix factorization
 SNMF  Sparse nonnegative matrix factorization based on alternating nonnegativity constrained least squares
 SNMNMF  Sparse networkregularized multiple nonnegative matrix factorization
Initialization Methods
 Random
 Fixed
 NNDSVD
 Random C
 Random VCol
Quality Measures
 Distance
 Residuals
 Connectivity matrix
 Consensus matrix
 Entropy of the fitted NMF model
 Dominant basis components computation
 Explained variance
 Feature score computation representing its specificity to basis vectors
 Computation of most basis specific features for basis vectors
 Purity
 Residual sum of squares (rank estimation)
 Sparseness
 Cophenetic correlation coefficient of consensus matrix (rank estimation)
 Dispersion
 Factorization rank estimation
 Selected matrix factorization method specific
Utils
 Fitted factorization model tracker across multiple runs
 Residuals tracker across multiple factorizations / runs
 Changes to previous version:
Initial Announcement on mloss.org.
 BibTeX Entry: Download
 URL: Project Homepage
 Supported Operating Systems: Agnostic
 Data Formats: Agnostic
 Tags: Nonnegative Matrix Factorization, Initialization Methods, Quality Measures
 Archive: download here
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