Bayesian Nonnegative Matrix Factorization With Dirichlet Process Mixtures

Nonnegative Matrix Factorization (NMF) is valuable in many applications of blind source separation, signal processing and machine learning. A number of algorithms that can infer nonnegative latent factors have been developed, but most of these assume a specific noise kernel. This is insufficient to...

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Veröffentlicht in:	IEEE transactions on signal processing 2020, Vol.68, p.3860-3870
Hauptverfasser:	Li, Caoyuan, Xie, Hong-Bo, Mengersen, Kerrie, Fan, Xuhui, Da Xu, Richard Yi, Sisson, Scott A., Van Huffel, Sabine
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Australia Bayes methods Bayesian analysis Brain modeling Computational modeling Cost function Dirichlet problem Dirichlet process Electroencephalography Factorization Gaussian mixture model Image classification Machine learning Model testing Muscles Noise nonnegative matrix factorization nonparametric Bayesian methods Nonparametric statistics Probabilistic models Signal processing variational Bayes
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