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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qiang Chen Shuicheng Yan Tian-Tsong Ng |
| Copyright Year | 2010 |
| Description | Author affiliation: Computer Vision and Image Understanding Department, Institute for Infocomm Research, Singapore (Tian-Tsong Ng) || Department of Electrical and Computer Engineering, National University of Singapore, Singapore (Qiang Chen; Shuicheng Yan) |
| Abstract | In practice, nonnegative data factorization is often performed for data dimensionality reduction prior to a classification task using a classifier which is effective in low dimensional space such as nearest neighbor classifier. In this work, we propose a novel formulation to learn a multi-class classifier directly through a supervised nonnegative data factorization. This new formulation has the following properties: 1) the nonnegative data matrix is approximated as the product of a nonnegative basis matrix and a coefficient matrix where the nonnegative bases distinctively capture the common characteristics of all classes apart from that specific to individual classes; 2) a regularization term is imposed on nonnegative data factorization so that each datum can be predominantly reconstructed by the common basis vectors and its corresponding class-specific basis vectors; and 3) the coefficient vector for each datum is assumed to be transformed from a mapped kernel space, and the l2 norm of the class-specific coefficients reveals the relative confidence of classes, which then directly leads to a multi-class classifier. We also present an iterative optimization technique for our formulation and analytically show its convergence property. Extensive experiments on face recognition, head pose estimation, and handwritten digit recognition tasks clearly demonstrate the advantages of the proposed classifier over the conventional two-step approach of nonnegative data factorization followed by a classic classifier. |
| Starting Page | 3562 |
| Ending Page | 3569 |
| File Size | 322319 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5539940 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Testing Face recognition Nearest neighbor searches Kernel Principal component analysis Independent component analysis Embedded computing Computer vision Image reconstruction Convergence |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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