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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yun Fu Huang, T.S. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of ECE, Univ. of Illinois at Urbana-Champaign, Urbana, IL (Yun Fu; Huang, T.S.) |
| Abstract | To design geometrically motivated approaches for classifying the high-dimensional data, we propose to learn a discriminant subspace using Correlation Embedding Analysis (CEA). This novel algorithm enhances its discriminant power by incorporating both correlational graph embedding and Fisher criterion. In a geometric interpretation, it projects the high- dimensional data onto a hypersphere and preserves intrinsic neighbor relations with the Pearson correlation metric. After the embedding, resulting data pairs from the same class are forced to enhance their correlation affinity, whereas neighboring points of different class are forced to reduce their correlation affinity at the same time. The feature learned by CEA is tolerable to scaling or outlier. Experiments on face recognition demonstrate the effectiveness and advantage of the CEA. |
| Starting Page | 1696 |
| Ending Page | 1699 |
| File Size | 160788 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424417650 |
| ISSN | 15224880 |
| DOI | 10.1109/ICIP.2008.4712100 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-10-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face recognition Pattern classification Linear discriminant analysis Euclidean distance Labeling Gaussian distribution Laplace equations Training data Linear approximation Algorithm design and analysis discriminant analysis Correlation embedding analysis graph embedding subspace learning |
| Content Type | Text |
| Resource Type | Article |
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