Paper Info
Title
Graph regularized multiset canonical correlations with applications to joint feature extraction.
Abstract
Multiset canonical correlation analysis (MCCA) is a powerful technique for analyzing linear correlations among multiple representation data. However, it usually fails to discover the intrinsic geometrical and discriminating structure of multiple data spaces in real-world applications. In this paper, we thus propose a novel algorithm, called graph regularized multiset canonical correlations (GrMCCs), which explicitly considers both discriminative and intrinsic geometrical structure in multiple representation data. GrMCC not only maximizes between-set cumulative correlations, but also minimizes local intraclass scatter and simultaneously maximizes local interclass separability by using the nearest neighbor graphs on within-set data. Thus, it can leverage the power of both MCCA and discriminative graph Laplacian regularization. Extensive experimental results on the AR, CMU PIE, Yale-B, AT&T, and ETH-80 datasets show that GrMCC has more discriminating power and can provide encouraging recognition results in contrast with the state-of-the-art algorithms.
Year
DOI
Venue
2014
10.1016/j.patcog.2014.06.016
Pattern Recognition
Keywords
Field
DocType
Pattern recognition,Canonical correlation analysis,Multiset canonical correlations,Graph embedding,Feature extraction
k-nearest neighbors algorithm,Laplacian matrix,Pattern recognition,Canonical correlation,Multiset,Graph embedding,Feature extraction,Regularization (mathematics),Artificial intelligence,Discriminative model,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
47
12
0031-3203
Citations 
PageRank 
References 
8
0.47
36
Authors
2
Name
Order
Citations
PageRank
Yun-Hao Yuan123522.18
Quan-Sen Sun214912.49