Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractional-Step Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis
The Journal of Machine Learning Research
Kernel maximum scatter difference based feature extraction and its application to face recognition
Pattern Recognition Letters
Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Letters: Feature extraction using fuzzy inverse FDA
Neurocomputing
Face recognition using a fuzzy fisherface classifier
Pattern Recognition
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Computers & Mathematics with Applications
Hi-index | 0.01 |
In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of date. In this paper, a novel feature extraction criterion, fuzzy maximum margin criterion (FMMC), is proposed by means of the maximum margin criterion (MMC) and fuzzy set theory. More specifically, the between-class and within-class fuzzy scatter matrices are redefined by incorporating the membership degrees of samples which relates the samples distribution information; then the feature extraction criterion maximized the average margin between classes after dimensionality reduction is applied. Furthermore, we utilize the generalized singular value decomposition (GSVD) to the criterion, which make the algorithm more effective; for nonlinear separated problems, we extend the kernel extension of FMMC with positive definite kernels. The effective of the novel criterion for linear and nonlinear separated problems is illustrated by experiments.