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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Fei Wang Changshui Zhang |
| Copyright Year | 2007 |
| Description | Author affiliation: Tsinghua Univ., Beijing (Fei Wang; Changshui Zhang) |
| Abstract | A novel algorithm called Average Neighborhood Margin Maximization (ANMM) is proposed for supervised linear feature extraction. For each data point, ANMM aims at pulling the neighboring points with the same class label towards it as near as possible, while simultaneously pushing the neighboring points with different labels away from it as far as possible. We will show that features extracted from ANMM can separate the data from different classes well, and it avoids the small sample size problem existed in traditional Linear Discriminant Analysis (LDA). The kernelized (nonlinear) counterpart of ANMM is also established in this paper. Moreover, as in many computer vision applications the data are more naturally represented by higher order tensors (e.g. images and videos), we develop a tensorized (multilinear) form of ANMM, which can directly extract features from tensors. The experimental results of applying ANMM to face recognition are presented to show the effectiveness of our method. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 528350 |
| Page Count | 8 |
| File Format | |
| ISBN | 1424411793 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2007.383124 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-06-17 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Feature extraction Linear discriminant analysis Principal component analysis Tensile stress Data mining Computer vision Scattering Pattern recognition Covariance matrix Kernel |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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