Project details for nimfa A Python Library for Nonnegative Matrix Factorization

Screenshot nimfa A Python Library for Nonnegative Matrix Factorization 1.0

by marinkaz - March 22, 2012, 02:38:18 CET [ Project Homepage BibTeX Download ]

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Nimfa is an open-source Python library that provides a unified interface to nonnegative matrix factorization algorithms. It includes implementations of state-of-the-art 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 / Kullback-Leibler 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 network-regularized multiple nonnegative matrix factorization

Initialization Methods

  • Random
  • Fixed
  • 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


  • Fitted factorization model tracker across multiple runs
  • Residuals tracker across multiple factorizations / runs
Changes to previous version:

Initial Announcement on

BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Agnostic
Tags: Nonnegative Matrix Factorization, Initialization Methods, Quality Measures
Archive: download here


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