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Semi-paired Probabilistic Canonical Correlation Analysis

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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https://hal.inria.fr/hal-01383310
Contributor : Hal Ifip <>
Submitted on : Tuesday, October 18, 2016 - 2:51:42 PM
Last modification on : Thursday, March 5, 2020 - 5:41:01 PM

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Bo Zhang, Jie Hao, Gang Ma, Jinpeng Yue, Zhongzhi Shi. Semi-paired Probabilistic Canonical Correlation Analysis. 8th International Conference on Intelligent Information Processing (IIP), Oct 2014, Hangzhou, China. pp.1-10, ⟨10.1007/978-3-662-44980-6_1⟩. ⟨hal-01383310⟩

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