Abstract
CCA is a powerful tool for analyzing paired multi-view data. However, when facing semi-paired multi-view data which widely exist in real-world problems, CCA usually performs poorly due to its requirement of data pairing between different views in nature. To cope with this problem, we propose a semi-paired variant of CCA named SemiPCCA based on the probabilistic model for CCA. Experiments with artificially generated samples demonstrate the effectiveness of the proposed method.
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CITATION STYLE
Zhang, B., Hao, J., Ma, G., Yue, J., & Shi, Z. (2014). Semi-paired probabilistic canonical correlation analysis. In IFIP Advances in Information and Communication Technology (Vol. 432, pp. 1–10). Springer New York LLC. https://doi.org/10.1007/978-3-662-44980-6_1
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