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| Adaptive Maximal Rejection Discriminant Analysis and Its Discriminant Vectors |
| GUO Zhi-Bo1,2,YAN Yun-Yang3,YANG Jing-Yu2,ZHAO Chun-Xia2 |
1.College of Information Engineering,Yangzhou University,Yangzhou 225009
2.College of Computer Science and Technology,Nanjing University of Science and Technology,Nanjing 210094
3.College of Computer Science and Technology,Huaiyin Institute of Technology,Huaiyin 223003 |
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Abstract Aiming at the two-value output of weak classifiers and Non-orthogonal discriminant vectors on the MRC-Boosting, an adaptive maximal rejection discriminant analysis (AdaMRDA) is proposed to further improve the classification performance. Based on the Euclid distance between the extracted discriminant features and their expectation mean, an adaptive updating weights method is developed firstly, by which the latter discriminant vectors obtained are more beneficial for classification. Then the equation solving the optimal orthogonal discriminant vectors is given. Finally, the experimental results on 2 databases prove that AdaMRDA is superior to MRC-Boosting and related methods on classification performance.
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Received: 11 March 2009
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[1] Fisher R A. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 1936, 7: 179-188
[2] Foley D H, Sammon J W Jr. An Optimal Set of Discriminant Vectors. IEEE Trans on Computers, 1975, 24(3): 281-289
[3] Osuna E, Freund R, Girosi F. Training Support Vector Machines: An Application to Face Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Juan, Puerto Rico, 1997: 130-136
[4] Waring C A, Liu X W. Face Detection Using Spectral Histograms and SVMs. IEEE Trans on Systems, Man and Cybernetics, 2005, 35(3): 467-476
[5] Rowley H A. Neural Network-Based Human Face Detection. Ph.D Dissertation. Pittsburgh, USA: Carnegie Mellon University. Computer Science Department, 1999
[6] Féraud R, Bernier O J, Viallet J E, et al. A Fast and Accurate Face Detector Based on Neural Networks. IEEE Trans on Pattern Analysis and Machine Intelligence, 2001, 23(1): 42-53
[7] Liu C J. A Bayesian Discriminating Features Method for Face Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 2003, 25(6): 725-740
[8] Freund Y, Schapire R E. Experiments with a New Boosting Algorithm // Proc of the 13th Conference on Machine Learning. Bari, Italy, 1996: 148-156
[9] Violas P, Jones M. Rapid Object Detection Using a Boosted Cascade of Simple Features // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Cambridge, USA, 2001, Ⅰ: 511-518
[10] Elad M, Hel-Or Y, Keshet R. Pattern Detection Using a Maximal Rejection Classifier. Pattern Recognition Letters, 2002, 23(12): 1459-1471
[11] Xu Xun, Huang T S. Face Recognition with MRC-Boosting // Proc of the 10th IEEE International Conference on Computer Vision. Beijing, China, 2005: 1770-1777
[12] Duchene J, Leclercq S. An Optimal Transformation for Discriminant and Principal Component Analysis. IEEE Trans on Pattern Analysis and Machine Intelligence, 1988, 10(6): 978-983 |
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2010, Vol. 23 
