Journal
NEUROCOMPUTING
Volume 70, Issue 7-9, Pages 1547-1553Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2006.11.007
Keywords
manifold learning; dimensionality reduction; linear local tangent space alignment (LLTSA); face recognition
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In this paper. linear local tangent space alignment (LLTSA), as a novel linear dimensionality reduction algorithm, is proposed. It uses the tangent space in the neighborhood of a data point to represent the local geometry, and then aligns those local tangent spaces in the low-dimensional space which is linearly mapped from the raw high-dimensional space. Since images of faces often belong to a manifold of intrinsically low dimension, we develop LLTSA algorithm for effective face manifold learning and recognition. Comprehensive comparisons and extensive experiments show that LLTSA achieves much higher recognition rates than a few competing methods. (c) 2007 Elsevier B.V. All rights reserved.
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