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Exponential Family Tensor Factorization: An Online Extension and Applications
http://hdl.handle.net/10445/6861
http://hdl.handle.net/10445/68610f332b69-23d9-4dea-aeac-51dbbbfeda1b
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2013-04-15 | |||||
| タイトル | ||||||
| タイトル | Exponential Family Tensor Factorization: An Online Extension and Applications | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Hayashi, Kohei
× Hayashi, Kohei× 竹之内, 高志× Shibata, Tomohiro |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | In this paper, we propose a new probabilistic model of heterogeneously attributed multi-dimensional arrays. The model can manage heterogeneity by employing individual exponential family distributions for each attribute of the tensor array. Entries of the tensor are connected by latent variables and share information across the different attributes through the latent variables. The assumption of heterogeneity makes a Bayesian inference intractable, and we cast the EM algorithm approximated by the Laplace method and Gaussian process. We also extended the proposal algorithm for online learning. We apply our method to missing values prediction and anomaly detection problems and show that our method outperforms conventional approaches that do not consider heterogeneity. | |||||
| 書誌情報 |
Knowledge and Information Systems 発行日 2012-06-14 |
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| 査読有無 | ||||||
| 値 | あり/yes | |||||
| 研究業績種別 | ||||||
| 値 | 原著論文/Original Paper | |||||
| 単著共著 | ||||||
| 値 | 共著/joint | |||||
| 出版者 | ||||||
| 出版者 | Springer | |||||
