NDLI: Local discriminant embedding and its variants

Content Provider	IEEE Xplore Digital Library
Author	Hwann-Tzong Chen Huang-Wei Chang Tyng-Luh Liu
Copyright Year	2005
Description	Author affiliation: Inst. of Inf. Sci., Acad. Sinica, Taipei, Taiwan (Hwann-Tzong Chen; Huang-Wei Chang; Tyng-Luh Liu)
Abstract	We present a new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification. In our framework, the neighbor and class relations of data are used to construct the embedding for classification problems. The proposed algorithm learns the embedding for the submanifold of each class by solving an optimization problem. After being embedded into a low-dimensional subspace, data points of the same class maintain their intrinsic neighbor relations, whereas neighboring points of different classes no longer stick to one another. Via embedding, new test data are thus more reliably classified by the nearest neighbor rule, owing to the locally discriminating nature. We also describe two useful variants: two-dimensional LDE and kernel LDE. Comprehensive comparisons and extensive experiments on face recognition are included to demonstrate the effectiveness of our method.
Sponsorship	IEEE Comput. Soc
Starting Page	846
Ending Page	853
File Size	254431
Page Count	8
File Format	PDF
ISBN	0769523722
ISSN	10636919
DOI	10.1109/CVPR.2005.216
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Testing Training data Linear discriminant analysis Principal component analysis Pattern classification Kernel Nearest neighbor searches Face recognition Information science Maintenance
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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