Bayesian Independent Component Analysis under Hierarchical Model on Independent Components

Kai Asaba, Shota Saito, Shunsuke Horii, Toshiyasu Matsushima

研究成果: Conference contribution

抄録

Independent component analysis (ICA) deals with the problem of estimating unknown latent variables (independent components) from observed data. One of the previous studies of ICA assumes a Laplace distribution on independent components. However, this assumption makes it difficult to calculate the posterior distribution of independent components. On the other hand, in the problem of sparse linear regression, several studies have approximately calculated the posterior distribution of parameters by assuming a hierarchical model expressing a Laplace distribution. This paper considers ICA in which a hierarchical model expressing a Laplace distribution is assumed on independent components. For this hierarchical model, we propose a method of calculating the approximate posterior distribution of independent components by using a variational Bayes method. Through some experiments, we show the effectiveness of our proposed method.

本文言語English
ホスト出版物のタイトル2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ959-962
ページ数4
ISBN(電子版)9789881476852
DOI
出版ステータスPublished - 2019 3 4
イベント10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Honolulu, United States
継続期間: 2018 11 122018 11 15

出版物シリーズ

名前2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings

Conference

Conference10th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018
CountryUnited States
CityHonolulu
Period18/11/1218/11/15

ASJC Scopus subject areas

  • Information Systems

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