@inproceedings{648a777edefd4a7abf483c0facec6af9,
title = "Nonparametric bayesian nonnegative matrix factorization",
abstract = "Nonnegative Matrix Factorization (NMF) is an important tool in machine learning for blind source separation and latent factor extraction. Most of existing NMF algorithms assume a specific noise kernel, which is insufficient to deal with complex noise in real scenarios. In this study, we present a hierarchical nonparametric nonnegative matrix factorization (NPNMF) model in which the Gaussian mixture model is used to approximate the complex noise distribution. The model is cast in the nonparametric Bayesian framework by using Dirichlet process mixture to infer the necessary number of Gaussian components. We derive a mean-field variational inference algorithm for the proposed nonparametric Bayesian model. Experimental results on both synthetic data and electroencephalogram (EEG) demonstrate that NPNMF performs better in extracting the latent nonnegative factors in comparison with state-of-the-art methods.",
keywords = "Dirichlet process, Gaussian mixture model, Nonnegative matrix factorization, Nonparametric Bayesian methods, Variational Bayes",
author = "Xie, {Hong Bo} and Caoyuan Li and Kerrie Mengersen and Shuliang Wang and Xu, {Richard Yi Da}",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020 ; Conference date: 02-09-2020 Through 04-09-2020",
year = "2020",
doi = "10.1007/978-3-030-57524-3_11",
language = "English",
isbn = "9783030575236",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "132--141",
editor = "Vicenc Torra and Yasuo Narukawa and Jordi Nin and N{\'u}ria Agell",
booktitle = "Modeling Decisions for Artificial Intelligence - 17th International Conference, MDAI 2020, Proceedings",
address = "Germany",
}