NIMFA: a python library for nonnegative matrix factorization

Authors:
Marinka Žitnik;Blaž Zupan
Affiliations:
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia;Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
Venue:
The Journal of Machine Learning Research
Year:
2012

Citing 8
Cited 1

Nonsmooth Nonnegative Matrix Factorization (nsNMF)

IEEE Transactions on Pattern Analysis and Machine Intelligence
Projected Gradient Methods for Nonnegative Matrix Factorization

Neural Computation
SVD based initialization: A head start for nonnegative matrix factorization

Pattern Recognition
Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis

Bioinformatics
Binary Matrix Factorization with Applications

ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Bayesian Non-negative Matrix Factorization

ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation

Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules

Bioinformatics

Comment-based multi-view clustering of web 2.0 items

Proceedings of the 23rd international conference on World wide web

Quantified Score

Hi-index	0.00

Visualization

Abstract

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. It supports both dense and sparse matrix representation. NIMFA's component-based implementation and hierarchical design should help the users to employ already implemented techniques or design and code new strategies for matrix factorization tasks.