SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations

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概要

• 論文の詳細を見る

• 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.

• 2013-03-12

著者

• Katsuhiko Ishiguro

  NTT Communication Science Laboratories, NTT Corporation

• Akisato Kimura

  NTT Communication Science Laboratories, NTT Corporation

• Masashi Sugiyama

  Graduate School of Information Science and Engineering, Tokyo Institute of Technology

• Takuho Nakano

  NTT Communication Science Laboratories, NTT Corporation|Graduate School of Information Science and Technologies, the University of Tokyo

• Hirokazu Kameoka

  NTT Communication Science Laboratories, NTT Corporation|Graduate School of Information Science and Technologies, the University of Tokyo

• Hitoshi Sakano

  NTT Communication Science Laboratories, NTT Corporation

• Eisaku Maeda

  NTT Communication Science Laboratories, NTT Corporation

関連論文

• Designing Various Multivariate Analysis at Will via Generalized Pairwise Expression
• SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations
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• Salient Object Detection Based on Direct Density-ratio Estimation

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