| タイトル | Bayesian Independent Component Analysis under Hierarchical Model on Independent Components |
|---|---|
| 著者 | 浅葉海 、齋藤翔太 、堀井俊佑 、松嶋敏泰 |
| 年度 | 2018 |
| 形式 | 国際学会 |
| 分野 | 知識情報処理 |
| 掲載雑誌名 | Proceedings of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference |
| 掲載号・ページ | pp.959--962 |
| 掲載年 | 2018 |
| 掲載月 | 11 |
| アブスト （日本語） |
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2018年11月12日～15日（発表日：14日） Honolulu, USA 査読有 DOI: 10.23919/APSIPA.2018.8659578 |
| アブスト （英語） |
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. |
| 備考 （日本語） |
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| 備考 （英語） |
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