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SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations
https://ipsj.ixsq.nii.ac.jp/records/91278
https://ipsj.ixsq.nii.ac.jp/records/912781b6a79b8-74a0-475a-86b7-90b30eab58c4
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-TOM0601015.pdf (903.3 kB)
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Copyright (c) 2013 by the Information Processing Society of Japan
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| オープンアクセス | ||
| Item type | Trans(1) | |||||||
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| 公開日 | 2013-03-12 | |||||||
| タイトル | ||||||||
| タイトル | SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations | |||||||
| タイトル | ||||||||
| 言語 | en | |||||||
| タイトル | SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | [オリジナル論文] Canonical correlation analysis, semi-supervised learning, generalized eigenproblem, principal component analysis, multi-label prediction | |||||||
| 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 | |||||||
| 著者所属 | ||||||||
| NTT Communication Science Laboratories, NTT Corporation | ||||||||
| 著者所属 | ||||||||
| Graduate School of Information Science and Engineering, Tokyo Institute of Technology | ||||||||
| 著者所属 | ||||||||
| NTT Communication Science Laboratories, NTT Corporation／Graduate School of Information Science and Technologies, the University of Tokyo | ||||||||
| 著者所属 | ||||||||
| NTT Communication Science Laboratories, NTT Corporation／Graduate School of Information Science and Technologies, the University of Tokyo | ||||||||
| 著者所属 | ||||||||
| NTT Communication Science Laboratories, NTT Corporation | ||||||||
| 著者所属 | ||||||||
| NTT Communication Science Laboratories, NTT Corporation | ||||||||
| 著者所属 | ||||||||
| NTT Communication Science Laboratories, NTT Corporation | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| NTT Communication Science Laboratories, NTT Corporation | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| Graduate School of Information Science and Engineering, Tokyo Institute of Technology | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| NTT Communication Science Laboratories, NTT Corporation / Graduate School of Information Science and Technologies, the University of Tokyo | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| NTT Communication Science Laboratories, NTT Corporation / Graduate School of Information Science and Technologies, the University of Tokyo | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| NTT Communication Science Laboratories, NTT Corporation | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| NTT Communication Science Laboratories, NTT Corporation | ||||||||
| 著者所属(英) | ||||||||
| en | ||||||||
| NTT Communication Science Laboratories, NTT Corporation | ||||||||
| 著者名 |
Akisato, Kimura
Masashi, Sugiyama
Takuho, Nakano
Hirokazu, Kameoka
Hitoshi, Sakano
Eisaku, Maeda
Katsuhiko, Ishiguro
× Akisato, Kimura Masashi, Sugiyama Takuho, Nakano Hirokazu, Kameoka Hitoshi, Sakano Eisaku, Maeda Katsuhiko, Ishiguro
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| 著者名(英) |
Akisato, Kimura
Masashi, Sugiyama
Takuho, Nakano
Hirokazu, Kameoka
Hitoshi, Sakano
Eisaku, Maeda
Katsuhiko, Ishiguro
× Akisato, Kimura Masashi, Sugiyama Takuho, Nakano Hirokazu, Kameoka Hitoshi, Sakano Eisaku, Maeda Katsuhiko, Ishiguro
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| 論文抄録 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limited, which is often the case in practice. To cope with this problem, we propose a semi-supervised variant of CCA named SemiCCA that allows us to incorporate additional unpaired samples for mitigating overfitting. Advantages of the proposed method over previously proposed methods are its computational efficiency and intuitive operationality: it smoothly bridges the generalized eigenvalue problems of CCA and principal component analysis (PCA), and thus its solution can be computed efficiently just by solving a single eigenvalue problem as the original CCA. | |||||||
| 論文抄録(英) | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | Canonical correlation analysis (CCA) is a powerful tool for analyzing multi-dimensional paired data. However, CCA tends to perform poorly when the number of paired samples is limited, which is often the case in practice. To cope with this problem, we propose a semi-supervised variant of CCA named SemiCCA that allows us to incorporate additional unpaired samples for mitigating overfitting. Advantages of the proposed method over previously proposed methods are its computational efficiency and intuitive operationality: it smoothly bridges the generalized eigenvalue problems of CCA and principal component analysis (PCA), and thus its solution can be computed efficiently just by solving a single eigenvalue problem as the original CCA. | |||||||
| 書誌レコードID | ||||||||
| 収録物識別子タイプ | NCID | |||||||
| 収録物識別子 | AA11464803 | |||||||
| 書誌情報 |
情報処理学会論文誌数理モデル化と応用（TOM） 巻 6, 号 1, p. 128-135, 発行日 2013-03-12 |
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| ISSN | ||||||||
| 収録物識別子タイプ | ISSN | |||||||
| 収録物識別子 | 1882-7780 | |||||||
| 出版者 | ||||||||
| 言語 | ja | |||||||
| 出版者 | 情報処理学会 | |||||||
