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.

• Type: Conference
• Year: 2006
• Where: ICIP
• Authors: Jian Xia, Dit-Yan Yeung, Guang Dai
• Keywords: Data Representation Problems, ICIP 2006, Image Processing, Multiple Interrelated Subspaces, Original Lde, Subspace Learning Method, Third-order Tensor Representation