文章基本信息

标题：SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations
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作者：Akisato Kimura ; Masashi Sugiyama ; Takuho Nakano 等
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2013
卷号：8
期号：2
页码：311-318
DOI：10.11185/imt.8.311
出版社：Information and Media Technologies Editorial Board
摘要：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.
关键词：Canonical correlation analysis;semi-supervised learning;generalized eigenproblem;principal component analysis;multi-label prediction