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 "SemiCCA" that allows us to incorporate additional unpaired samples for mitigating overfittng. The proposed method smoothly bridges the eigenvalue problems of CCA and principal component analysis (PCA), and thus its solution can be computed efficiently just by solving a single (generalized) eigenvalue problem as the original CCA. Preliminary experiments with artificially generated samples and PASCAL VOC data sets demonstrate the effectiveness of the proposed method.

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Akisato Kimura, Hirokazu Kameoka, Masashi Sugiyama, Eisaku Maeda, Hitoshi Sakano, Katsuhiko Ishiguro
• Keywords: Canonical Correlation Analysis, Computer Vision, Eigenvalue Problem, ICPR 2010, Multi-dimensional Paired Data