NDLI: A New Locality-Preserving Canonical Correlation Analysis Algorithm for Multi-View Dimensionality Reduction

Content Provider	Springer Nature Link
Author	Wang, Fengshan Zhang, Daoqiang
Copyright Year	2012
Abstract	Canonical correlation analysis (CCA) is a well-known technique for extracting linearly correlated features from multiple views (i.e., sets of features) of data. Recently, a locality-preserving CCA, named LPCCA, has been developed to incorporate the neighborhood information into CCA. Although LPCCA is proved to be better in revealing the intrinsic data structure than CCA, its discriminative power for subsequent classification is low on high-dimensional data sets such as face databases. In this paper, we propose an alternative formulation for integrating the neighborhood information into CCA and derive a new locality-preserving CCA algorithm called ALPCCA, which can better discover the local manifold structure of data and further enhance the discriminative power for high-dimensional classification. The experimental results on both synthetic and real-world data sets including multiple feature data set and face databases validate the effectiveness of the proposed method.
Starting Page	135
Ending Page	146
Page Count	12
File Format	PDF
ISSN	13704621
Journal	Neural Processing Letters
Volume Number	37
Issue Number	2
e-ISSN	1573773X
Language	English
Publisher	Springer US
Publisher Date	2012-07-27
Publisher Place	Boston
Access Restriction	Subscribed
Subject Keyword	Locality preserving projection Canonical correlation analysis Multi-view dimensionality reduction High-dimensional classification Artificial Intelligence (incl. Robotics) Statistical Physics, Dynamical Systems and Complexity Computational Intelligence
Content Type	Text
Resource Type	Article
Subject	Neuroscience Artificial Intelligence Computer Networks and Communications Software

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