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
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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2010, Vol. 23 
