Cost-Sensitive Dimensionality Reduction And Its Application In Face Recognition

Conventional dimensionality reduction algorithms aim to attain low recognition errors, assuming that same misclassification loss of different misclassifications. In some real-world applications, this assumption may not hold. For example, in the door-locker based on face recognition, there has impostor and gallery person. The loss of misclassification impostor as gallery person are larger than misclassification gallery person as impostor, while the loss of misclassification gallery person as impostor will be larger than misclassification as other gallery person. So, this thesis proposes cost-sensitive dimensionality reduction. The main contributions of this thesis are as follows:1. A method called Weighted Cost-Sensitive Local Perserving Projection (WCSLPP) is introduced. Traditional Local Perserving Projection (LPP) aims to attain minimal misclassification error rate, and its projection direction will be influenced by imbalanced data. So this thesis embeds misclassification costs in LPP model, and defines the WCSLPP model which satisfies the minimal misclassification loss criterion. Besides, to deal with class imbalance problem, WCSLPP defines a weighted function to balance the contribution of different classes to the projection direction. The experimental results on face datasets show the superiority of WCSLPP.2. An algorithm named Pairwise Costs in Linear Discriminant Analysis (PCLDA) is proposed. By embeding a weighted function in the Linear Discriminant Analysis (LDA), PCLDA approximates the pairwise Bayesian risk criterion and effectively restrain the influence of outliers to the projection direction. Besides, considering the different class distribution density problem in data sets, PCLDA defines an important function to balance the contribution of different classes to the projection direction. The experimental results on face datasets demonstrate the effectiveness of PCLDA.3. An approach called Pairwise Costs in SubClass Discriminant Analysis (PCSCDA) is suggested. By analyzing the door-clocker based on face recognition, this thesis recognizes the door-clocker as a cost-sensitive subclass learning problem, then embeds the subclass information and misclassification costs in the framework of discriminant analysis at the same time, and proposes the PCSCDA algorithm approximates the pairwise Bayesian risk criterion.The experimental results on face datasets show the validity of PCSCDA.4. We propose a method named Pairwise Costs in Semi-supervised Discriminant Analysis (PCSDA). In real-world applications, there have a large number of unlabeled data and it is difficult to attain labeled data. To effectively utilize the information of unlabeled data, PCSDA uses l2approach to predict the label of unlabeled data. Compared with other label propagation strategies,l2approach has higher prediction accuracy and lower time complexity. Then by embeding a weighted function in LDA model, PCSDA approximates the pairwise accuracy criterion, and improves the discriminative ability of the projection direction. The experimental results on face datasets demonstrate the superiority of PCSDA.5. We develop an algorithm called Sample-Dependent Cost-Sensitive Semi-Supervised Support Vector Machine (SCS-LapSVM). In real-world applications, there may have cost-sensitive problem, the data sets of which may have class imbalance problem, a large number of unlabeled data and noise samples. In view of these situations in the data sets, SCS-LapSVM embeds the misclassification costs considering class imbalance problem in the hinge loss and Laplacian regularization of Laplacian support vector machine, on the basis of label propagation. Considering the effect on decision hypersphere of the noise samples, SCS-LapSVM defines an example-dependent cost which makes the weights of noise samples lower. The experimental results on UCI and NASA data sets show the effectiveness of SCS-LapSVM.