 |
Technical Program
Paper Detail
| Paper: | MP-P5.3 |
| Session: | Machine Learning for Image and Video Classification |
| Time: | Monday, October 9, 14:20 - 17:00 |
| Presentation: |
Poster
|
| Title: |
LOCAL DISCRIMINANT EMBEDDING WITH TENSOR REPRESENTATION |
| Authors: |
Jian Xia; Hong Kong University of Science and Technology | | | | | Dit-Yan Yeung; Hong Kong University of Science and Technology | | | | | Guang Dai; Hong Kong University of Science and Technology | | |
| 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. |
|
