NDLI: Semi-supervised two-dimensional manifold learning based on pair-wise constraints

Content Provider	IEEE Xplore Digital Library
Author	Xue Wei Wang Zheng-qun Li Feng Zhou Zhong-xia
Copyright Year	2014
Description	Author affiliation: Dept. of Inf. & Eng., Yang Zhou Univ., Yangzhou, China (Xue Wei; Wang Zheng-qun; Li Feng; Zhou Zhong-xia)
Abstract	Considering the huge calculated amount of eigen-decomposition in one-dimensional Linear local tangent space alignment (LLTSA), this paper proposed a Semi-supervised two-dimensional manifold learning based on pair-wise constraints (2D-PCLTSA). 2D-PCLTSA adopts two-dimensional image matrices as the samples to extract image feature information, and uses pair-wise constraints as supervised information. 2D-PCLTSA preserves the feature information in the sample set while taking advantage of the supervised information effectively. Through the experiments on YALE and ORL, 2D-PCLTSA outperforms based on traditional dimensionality reduction algorithms with maximum average recognition rate by 2.85% and 6.25% respectively. Especially, our algorithm could keep well classification performance with a few constraints.
Starting Page	4807
Ending Page	4811
File Size	209963
Page Count	5
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6895753
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Training Manifolds eigen-decomposition face recognition Feature extraction Vectors semi-supervised learning tangent space Matrix converters Covariance matrices Principal component analysis pair-wise constraints
Content Type	Text
Resource Type	Article

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