Abstract
Constrained maximum variance mapping (CMVM) based on the multi-manifold learning is an efficiency method for feature extraction. CMVM preserves the local manifold structure by keep the sum of the distances of samples unchanged, but ignores the local label information of the samples, which is very important to the recognition. To tackle the shortage, we propose a new method called supervised constrained maximum variance mapping (SCMVM), which projects the local structure into feature space by a linear map. SCMVM combines the Euclidean distance with the label information in local structure and maximizing the distance of samples with different classes. Because consider the local label information, the efficiency of recognition enhances clearly. In this paper, we take experiments on Yale face database and USPS handwriting database using CMVM and SCMVM, and compare the efficiency.
| Original language | English |
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| Title of host publication | International Conference on Automatic Control and Artificial Intelligence, ACAI 2012 |
| Publisher | IEEE |
| Pages | 1049-1052 |
| Number of pages | 4 |
| ISBN (Print) | 9781849195379 |
| DOIs | |
| Publication status | Published - 2012 |
| Externally published | Yes |
| Event | International Conference on Automatic Control and Artificial Intelligence, ACAI 2012 - Xiamen, China Duration: 3 Mar 2012 → 5 Mar 2012 |
Conference
| Conference | International Conference on Automatic Control and Artificial Intelligence, ACAI 2012 |
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| Country/Territory | China |
| City | Xiamen |
| Period | 3/03/12 → 5/03/12 |
Bibliographical note
This work is both partly supported by the NSF of China (No. 61170040, No. 60903088, No. 60903089), and by the Scientific Research Foundation of Education Department of Hebei Province (No. 2009312, No.2009410).Keywords
- Constrained maximum variance mapping
- Feature extraction
- Manifold learning
- Supervise