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AdaBoost has proved to be an effective method to improve the performance of base classifiers both theoretically and empirically. However, previous studies have shown that AdaBoost might suffer from the overfitting problem, especially for noisy data. In addition, it still needs much time to train the classifier using AdaBoost. In this paper, we focus on designing an algorithm named Heritance AdaBoostRF...
In Two-Dimensional Linear Discriminant Analysis (2DLDA), it is satisfied that within-class covariance matrixes are equal; while in Two-Dimensional Heteroscedastic Discriminant Analysis (2DHDA), within-class covariance matrixes are heteroscedastic. Based on the characters of 2DLDA and 2DHDA, Weighted Two-Dimensional Heteroscedastic Discriminant Analysis (W2DHDA) is introduced and used in face recognition,...
Kernel method is a nonlinear feature extraction approach. Firstly, the samples in the original feature space are transformed into a higher dimensional feature space by nonlinear mapping. Then, linear approaches are used in the higher dimensional feature space, and thus nonlinear features of original samples are extracted. The Heteroscedastic Discriminant Analysis (HDA), in which the equal within-class...
Kernel method is a nonlinear feature extraction approach. Firstly, the samples in the original feature space are transformed into a higher dimensional feature space by nonlinear mapping. Then, linear approaches are used in the higher dimensional feature space, and thus nonlinear features of original samples are extracted. The Heteroscedastic Discriminant Analysis (HDA), in which the equal within-class...
In this paper, a novel discriminant analysis named two-dimensional Heteroscedastic Discriminant Analysis (2DHDA) is presented for face recognition. In 2DHDA, small sample size problem (S3 problem) of Heteroscedastic Discriminant Analysis (HAD) is overcome. Firstly, the criterion of 2DHDA is defined according to that of 2DLDA. Secondly, criterion of 2DHDA, log and rearranging terms are taken, and then...
On the basis of two dimensional principal component analysis, an improved two dimensional principal component analysis (I2DPCA) is presented for face recognition. Firstly, the criterion functions of global and between class scatters of projection features are defined. Secondly, the two defined criterion functions are fused by way of multiplication or addition. Therefore, the criterion function of...
Face recognition has been of interest to a growing number of researchers, and many algorithms are presented. However, the recognition rate will be significantly reduced in the case of large sample size and greater facial expression changes. In this paper, 2DPCA algorithm is used for features extraction and Boosting by filtering method is used to choose training samples. Then, the expert systems of...
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