Algorithms for Nonnegative Matrix Factorization with the β-Divergence

DOI10.1162/NECO_a_00168zbMath1231.65072arXiv1010.1763OpenAlexW2114508388MaRDI QIDQ3116934

Cédric Févotte, Jérôme Idier

Publication date: 14 February 2012

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1010.1763

Mathematics Subject Classification ID

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Cites Work

• Families of alpha-, beta- and gamma-divergences: flexible and robust measures of similarities
• Algorithms and applications for approximate nonnegative matrix factorization
• Nonnegative matrix factorization for spectral data analysis
• Robust Blind Source Separation by Beta Divergence
• A Generalized Divergence Measure for Nonnegative Matrix Factorization
• Nonnegative Matrix Factorization with the Itakura-Saito Divergence: With Application to Music Analysis
• Robust and efficient estimation by minimising a density power divergence
• Learning the parts of objects by non-negative matrix factorization
• Projected Gradient Methods for Nonnegative Matrix Factorization
• A general method to devise maximum-likelihood signal restoration multiplicative algorithms with non-negativity constraints.

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