@article{oai:ipsj.ixsq.nii.ac.jp:00091278,
 author = {Akisato, Kimura and Masashi, Sugiyama and Takuho, Nakano and Hirokazu, Kameoka and Hitoshi, Sakano and Eisaku, Maeda and Katsuhiko, Ishiguro and Akisato, Kimura and Masashi, Sugiyama and Takuho, Nakano and Hirokazu, Kameoka and Hitoshi, Sakano and Eisaku, Maeda and Katsuhiko, Ishiguro},
 issue = {1},
 journal = {情報処理学会論文誌数理モデル化と応用（TOM）},
 month = {Mar},
 note = {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.},
 pages = {128--135},
 title = {SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations},
 volume = {6},
 year = {2013}
}