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
- Random C
- Random VCol
- 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
- Residual sum of squares (rank estimation)
- Cophenetic correlation coefficient of consensus matrix (rank estimation)
- 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 mloss.org.
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