Laplacian multiset canonical correlations for multiview feature extraction and image recognition

Yuan, Yun-Hao, Li, Yun, Shen, Xiao-Bo, Sun, Quan-Sen and Yang, Jin-Long (2015) Laplacian multiset canonical correlations for multiview feature extraction and image recognition. Multimedia Tools and Applications, 76 1: 1-25. doi:10.1007/s11042-015-3070-y

Author Yuan, Yun-Hao
Li, Yun
Shen, Xiao-Bo
Sun, Quan-Sen
Yang, Jin-Long
Title Laplacian multiset canonical correlations for multiview feature extraction and image recognition
Journal name Multimedia Tools and Applications   Check publisher's open access policy
ISSN 1573-7721
Publication date 2015-11-18
Sub-type Article (original research)
DOI 10.1007/s11042-015-3070-y
Open Access Status Not Open Access
Volume 76
Issue 1
Start page 1
End page 25
Total pages 25
Place of publication New York, NY, United States
Publisher Springer New York
Language eng
Subject 1712 Software
2214 Media Technology
1708 Hardware and Architecture
1705 Computer Networks and Communications
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.
Keyword Image recognition
Manifold learning
Multiset canonical correlations
Multiview dimensionality reduction
Multiview learning
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 61273251
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
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