http://swrc.ontoware.org/ontology#Article
SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations
en
[オリジナル論文] Canonical correlation analysis, semi-supervised learning, generalized eigenproblem, principal component analysis, multi-label prediction
NTT Communication Science Laboratories, NTT Corporation
Graduate School of Information Science and Engineering, Tokyo Institute of Technology
NTT Communication Science Laboratories, NTT Corporation／Graduate School of Information Science and Technologies, the University of Tokyo
NTT Communication Science Laboratories, NTT Corporation／Graduate School of Information Science and Technologies, the University of Tokyo
NTT Communication Science Laboratories, NTT Corporation
NTT Communication Science Laboratories, NTT Corporation
NTT Communication Science Laboratories, NTT Corporation
Akisato Kimura
Masashi Sugiyama
Takuho Nakano
Hirokazu Kameoka
Hitoshi Sakano
Eisaku Maeda
Katsuhiko Ishiguro
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.
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.
AA11464803
情報処理学会論文誌数理モデル化と応用（TOM）
6
1
128-135
2013-03-12
1882-7780