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Title: Local discriminant embedding with tensor representation
Authors: Xia, Jian
Yeung, Dit-Yan
Dai, Guang
Keywords: Learning systems
Pattern classification
Face recognition
Issue Date: Oct-2006
Citation: Proceedings 2006 IEEE Conference on Image Processing, ICIP 2006, 8-11 October, 2006, Atlanta, GA, USA, p. 929-932
Abstract: We present a subspace learning method, called Local Discriminant Embedding with Tensor representation (LDET), that addresses simultaneously the generalization and data representation problems in subspace learning. LDET learns multiple interrelated subspaces for obtaining a lower-dimensional embedding by incorporating both class label information and neighborhood information. By encoding each object as a second- or higher-order tensor, LDET can capture higher-order structures in the data without requiring a large sample size. Extensive empirical studies have been performed to compare LDET with a second- or third-order tensor representation and the original LDE on their face recognition performance. Not only does LDET have a lower computational complexity than LDE, but LDET is also superior to LDE in terms of its recognition accuracy.
Rights: © 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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