Semi-paired Probabilistic Canonical Correlation Analysis - Intelligent Information Processing VII (IIP 2014) Access content directly
Conference Papers Year : 2014

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.
Fichier principal
Vignette du fichier
978-3-662-44980-6_1_Chapter.pdf (529.39 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01383310 , version 1 (18-10-2016)

Licence

Attribution

Identifiers

Cite

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⟩
47 View
139 Download

Altmetric

Share

Gmail Facebook X LinkedIn More