Bidirectional semi-supervised learning with graphs
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Neighborhood Correlation Analysis for Semi-paired Two-View Data
Neural Processing Letters
Multi-view classification with cross-view must-link and cannot-link side information
Knowledge-Based Systems
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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 "Semi CCA" that allows us to incorporate additional unpaired samples for mitigating overfitting. 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.