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2006
IEEE

Local Discriminant Embedding with Tensor Representation

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Local Discriminant Embedding with Tensor Representation
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.
Jian Xia, Dit-Yan Yeung, Guang Dai
Added 22 Oct 2009
Updated 22 Oct 2009
Type Conference
Year 2006
Where ICIP
Authors Jian Xia, Dit-Yan Yeung, Guang Dai
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