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- Title
Laplacian multiset canonical correlations for multiview feature extraction and image recognition.
- Authors
Yuan, Yun-Hao; Li, Yun; Shen, Xiao-Bo; Sun, Quan-Sen; Yang, Jin-Long
- Abstract
Multiset canonical correlation analysis (MCCA) aims at revealing the linear correlations among multiple sets of high-dimensional data. Therefore, it is only a linear multiview dimensionality reduction technique and such a linear model is insufficient to discover the nonlinear correlation information hidden in multiview data. In this paper, we incorporate the local structure information into MCCA and propose a novel algorithm for multiview dimensionality reduction, called Laplacian multiset canonical correlations (LapMCCs), which simultaneously considers local within-view and local between-view correlations by using nearest neighbor graphs. This makes LapMCC capable of discovering the nonlinear correlation information among multiview data by combining many locally linear problems together. Moreover, we also develop an orthogonal version of LapMCC to preserve the metric structure. The proposed LapMCC method is applied to face and object image recognition. The experimental results on AR, Yale-B, AT&T, and ETH-80 databases demonstrate the superior performance of LapMCC compared to existing multiview dimensionality reduction methods.
- Subjects
LAPLACIAN matrices; IMAGE recognition (Computer vision); STATISTICAL correlation; LINEAR statistical models; GRAPH theory
- Publication
Multimedia Tools & Applications, 2017, Vol 76, Issue 1, p731
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-015-3070-y