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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2019

12.09.2018

Nonnegative Constrained Graph Based Canonical Correlation Analysis for Multi-view Feature Learning

verfasst von: Huibin Tan, Xiang Zhang, Long Lan, Xuhui Huang, Zhigang Luo

Erschienen in: Neural Processing Letters | Ausgabe 2/2019

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Abstract

Understanding and analyzing multi-view data is a fundamental research topic of feature learning for a wide range of practical applications such as image classification. Canonical correlation analysis (CCA) is a popular unsupervised method of analyzing multi-view data, which captures common subspace of two groups of variable sets by maximizing the correlations between them. However, traditional CCA ignores the underlying geometric structure within dataset, which shows great power in describing data distribution, and thus fails in some tasks such as classification. To handle this limitation, this paper proposes an improved CCA variant of Nonnegative Constrained Graph regularized CCA (NCGCCA). Specifically, we improve CCA to NCGCCA with the following two contributions. Firstly, we develop a nonnegative constrained graph based self-representation to explore the underlying group-wise structure within dataset. Secondly, based on the proposed informative representation, we offer a graph embedding schema to incorporate the underlying structure into CCA. Experiments of image classification on four face datasets including Yale, ORL, UMIST, and YaleB demonstrate the efficacy of NCGCCA compared with existing baseline CCA methods.
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Metadaten
Titel
Nonnegative Constrained Graph Based Canonical Correlation Analysis for Multi-view Feature Learning
verfasst von
Huibin Tan
Xiang Zhang
Long Lan
Xuhui Huang
Zhigang Luo
Publikationsdatum
12.09.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9904-7

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