Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Random Data: Analysis and Measurement Procedures
Random Data: Analysis and Measurement Procedures
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Robust De-noising by Kernel PCA
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
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
SIAM Journal on Matrix Analysis and Applications
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Rapid and Brief Communication: Fuzzy discriminant analysis with kernel methods
Pattern Recognition
Discriminant kernel and regularization parameter learning via semidefinite programming
Proceedings of the 24th international conference on Machine learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
2009 Special Issue: RKF-PCA: Robust kernel fuzzy PCA
Neural Networks
A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Fuzzy SVM for noisy data: a robust membership calculation method
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Segmentation of scanned insect footprints using ART2 for threshold selection
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Neural Networks
Neurocomputing
Regularized discriminant entropy analysis
Pattern Recognition
| Hi-index | 0.01 |
Linear discriminant analysis (LDA) is a simple but widely used algorithm in the area of pattern recognition. However, it has some shortcomings in that it is sensitive to outliers and limited to linearly separable cases. To solve these problems, in this paper, a non-linear robust variant of LDA, called robust kernel fuzzy discriminant analysis (RKFDA) is proposed. RKFDA uses fuzzy memberships to reduce the effect of outliers and adopts kernel methods to accommodate non-linearly separable cases. There have been other attempts to solve the problems of LDA, including attempts using kernels. However, RKFDA, encompassing previous methods, is the most general one. Furthermore, theoretical analysis and experimental results show that RKFDA is superior to other existing methods in solving the problems.