| Presentation | 2018-03-05 Bayesian Independent Component Analysis under Hierarchical Model on Latent Variables Kai Asaba, Shota Saito, Shunsuke Horii, Toshiyasu Matsushima, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Independent component analysis (ICA) deals with the problem of estimating unknown latent variables which generate the observed data. ICA has applications such as speech signal processing, time series analysis, and image feature extraction. A previous study of ICA assumes Laplace distribution on latent variables. However, this assumption makes it difficult to calculate the posterior distribution of latent variables. In the problem of sparse liner regression, on the other hand, several studies have approximately calculated the posterior distribution by assuming a hierarchical prior model representing Laplace distribution. This paper treats the problem of ICA and assumes a hierarchical prior model representing Laplace distribution on latent variables. Based on this hierarchical model, we propose a method of calculating the approximate posterior distribution on latent variables by using variational Bayes method. To compare our method and conventional ICA method, some experiments on synthetic data are performed. Through these experiments, we show the effectiveness of our proposed method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Independent Component Analysis / Hierarchical Model / Variational Bayes Method |
| Paper # | IBISML2017-97 |
| Date of Issue | 2018-02-26 (IBISML) |
| Conference Information | |
| Committee | IBISML |
|---|---|
| Conference Date | 2018/3/5(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nishijin Plaza, Kyushu University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Statisitical Mathematics, Machine Learning, Data Mining, etc. |
| Chair | Kenji Fukumizu(ISM) |
| Vice Chair | Masashi Sugiyama(Univ. of Tokyo) / Hisashi Kashima(Kyoto Univ.) |
| Secretary | Masashi Sugiyama(Nagoya Inst. of Tech.) / Hisashi Kashima(Univ. of Tokyo) |
| Assistant | Tomoharu Iwata(NTT) / Toshihiro Kamishima(AIST) |
| Paper Information | |
| Registration To | Technical Committee on Infomation-Based Induction Sciences and Machine Learning |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Bayesian Independent Component Analysis under Hierarchical Model on Latent Variables |
| Sub Title (in English) | |
| Keyword(1) | Independent Component Analysis |
| Keyword(2) | Hierarchical Model |
| Keyword(3) | Variational Bayes Method |
| 1st Author's Name | Kai Asaba |
| 1st Author's Affiliation | Waseda University(Waseda Univ.) |
| 2nd Author's Name | Shota Saito |
| 2nd Author's Affiliation | Waseda University(Waseda Univ.) |
| 3rd Author's Name | Shunsuke Horii |
| 3rd Author's Affiliation | Waseda University(Waseda Univ.) |
| 4th Author's Name | Toshiyasu Matsushima |
| 4th Author's Affiliation | Waseda University(Waseda Univ.) |
| Date | 2018-03-05 |
| Paper # | IBISML2017-97 |
| Volume (vol) | vol.117 |
| Number (no) | IBISML-475 |
| Page | pp.pp.49-53(IBISML), |
| #Pages | 5 |
| Date of Issue | 2018-02-26 (IBISML) |