Laplacian multiset canonical correlations for multiview feature extraction and image recognition

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• 作者：Yun-Hao Yuan ; Yun Li ; Xiao-Bo Shen ; Quan-Sen Sun…
• 关键词：Image recognition ; Multiset canonical correlations ; Manifold learning ; Multiview dimensionality reduction ; Multiview learning
• 刊名：Multimedia Tools and Applications
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：76
• 期：1
• 页码：731-755
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems;
• 出版者：Springer US
• ISSN：1573-7721
• 卷排序：76

文摘

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.