Paper Info
Title
Laplacian multiset canonical correlations for multiview feature extraction and image recognition.
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.
Year
DOI
Venue
2017
10.1007/s11042-015-3070-y
Multimedia Tools Appl.
Keywords
Field
DocType
Image recognition, Multiset canonical correlations, Manifold learning, Multiview dimensionality reduction, Multiview learning
k-nearest neighbors algorithm,Computer vision,Dimensionality reduction,Pattern recognition,Computer science,Multiset,Linear model,Canonical correlation,Feature extraction,Artificial intelligence,Nonlinear dimensionality reduction,Laplace operator
Journal
Volume
Issue
ISSN
76
1
1573-7721
Citations 
PageRank 
References 
7
0.47
47
Authors
5
Name
Order
Citations
PageRank
Yun-Hao Yuan123522.18
Yun Li244353.24
Xiao-Bo Shen320921.35
Quan-Sen Sun414912.49
Jinlong Yang5278.07