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| Orthogonal MFA and Uncorrelated MFA |
| YU Yao-Liang, ZHANG Li-Ming |
| Department of Electronics Engineering, School of Information Science and Engineering, Fudan University, Shanghai 200433 |
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Abstract Recently proposed marginal fisher analysis (MFA) has obtained better classification results than the traditional linear discriminant analysis (LDA). Based on the separability criterion of MFA, the orthogonal and uncorrelated restrictions are imposed on the base-vectors in this paper. An iterative algorithm for the proposed methods is given and it is proved theoretically that the separability of the proposed methods is better than that of the original MFA. Finally, experimental results on ORL and Yale databases validate the effectiveness of the proposed methods.
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Received: 30 July 2007
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2008, Vol. 21 
