SemiCCA: Ef cient semi-supervised learning of canonical correlations
نویسندگان
چکیده
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 semiCCAthat allows us to incorporate additional unpaired samples for mitigating over ttng. The proposed method smoothly bridges the eigenvalue problems of CCA and principal component analysis (PCA), and thus its solution can be computed ef ciently just by solving a single eigenvalue problem as the original CCA. Index TermsCanonical correlation analysis, semi-supervised learning, generalized eigenproblem, automatic image annotation
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