Measuring the VC-dimension of a learning machine
Neural Computation
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved-LDA based face recognition using both facial global and local information
Pattern Recognition Letters
Neighborhood discriminant projection for face recognition
Pattern Recognition Letters
A novel class-dependence feature analysis method for face recognition
Pattern Recognition Letters
A note on two-dimensional linear discriminant analysis
Pattern Recognition Letters
Boosting random subspace method
Neural Networks
Boosting k-nearest neighbor classifier by means of input space projection
Expert Systems with Applications: An International Journal
Face recognition using a fuzzy fisherface classifier
Pattern Recognition
Integrating Discriminant and Descriptive Information for Dimension Reduction and Classification
IEEE Transactions on Circuits and Systems for Video Technology
A novel training weighted ensemble (TWE) with application to face recognition
Applied Soft Computing
An improved hybrid approach to face recognition by fusing local and global discriminant features
International Journal of Biometrics
A sub-block-based eigenphases algorithm with optimum sub-block size
Knowledge-Based Systems
Radar target recognition based on fuzzy optimal transformation using high-resolution range profile
Pattern Recognition Letters
Computers & Mathematics with Applications
Hi-index | 0.00 |
In this paper, some studies have been made on the essence of fuzzy linear discriminant analysis (F-LDA) algorithm and fuzzy support vector machine (FSVM) classifier, respectively. As a kernel-based learning machine, FSVM is represented with the fuzzy membership function while realizing the same classification results with that of the conventional pair-wise classification. It outperforms other learning machines especially when unclassifiable regions still remain in those conventional classifiers. However, a serious drawback of FSVM is that the computation requirement increases rapidly with the increase of the number of classes and training sample size. To address this problem, an improved FSVM method that combines the advantages of FSVM and decision tree, called DT-FSVM, is proposed firstly. Furthermore, in the process of feature extraction, a reformative F-LDA algorithm based on the fuzzy k-nearest neighbors (FKNN) is implemented to achieve the distribution information of each original sample represented with fuzzy membership grade, which is incorporated into the redefinition of the scatter matrices. In particular, considering the fact that the outlier samples in the patterns may have some adverse influence on the classification result, we developed a novel F-LDA algorithm using a relaxed normalized condition in the definition of fuzzy membership function. Thus, the classification limitation from the outlier samples is effectively alleviated. Finally, by making full use of the fuzzy set theory, a complete F-LDA (CF-LDA) framework is developed by combining the reformative F-LDA (RF-LDA) feature extraction method and DT-FSVM classifier. This hybrid fuzzy algorithm is applied to the face recognition problem, extensive experimental studies conducted on the ORL and NUST603 face images databases demonstrate the effectiveness of the proposed algorithm.