SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations

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 Se...

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Bibliographic Details
Published inIPSJ Online Transactions Vol. 6; pp. 37 - 44
Main Authors Kimura, Akisato, Sugiyama, Masashi, Nakano, Takuho, Kameoka, Hirokazu, Sakano, Hitoshi, Maeda, Eisaku, Ishiguro, Katsuhiko
Format Journal Article
LanguageEnglish
Published Information Processing Society of Japan 2013
Subjects
Canonical correlation analysis
generalized eigenproblem
multi-label prediction
principal component analysis
semi-supervised learning
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Summary: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.
ISSN:1882-6660
1882-6660
DOI:10.2197/ipsjtrans.6.37