Feature selection based on kernel discriminant analysis

Authors:
Masamichi Ashihara;Shigeo Abe
Affiliations:
Graduate School of Science and Technology, Kobe University, Rokkodai, Nada, Kobe, Japan;Graduate School of Science and Technology, Kobe University, Rokkodai, Nada, Kobe, Japan
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

Citing 11
Cited 3

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Feature Selection via Concave Minimization and Support Vector Machines

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Dimensionality reduction via sparse support vector machines

The Journal of Machine Learning Research
Grafting: fast, incremental feature selection by gradient descent in function space

The Journal of Machine Learning Research
Variable selection using svm based criteria

The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods

The Journal of Machine Learning Research
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)

Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Generalized Discriminant Analysis Using a Kernel Approach

Neural Computation
FS_SFS: A novel feature selection method for support vector machines

Pattern Recognition
A novel approach to feature selection based on analysis of class regions

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Kernel discriminant analysis based feature selection

Neurocomputing
Sparse Least Squares Support Vector Machines by Forward Selection Based on Linear Discriminant Analysis

ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Evaluation of feature selection by multiclass kernel discriminant analysis

ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

For two-class problems we propose two feature selection criteria based on kernel discriminant analysis. The first one is the objective function of kernel discriminant analysis (KDA) and the second one is the KDA-based exception ratio. We show that the objective function of KDA is monotonic for the deletion of features, which ensures stable feature selection. The KDA-based exception ratio defines the overlap between classes in the one-dimensional space obtained by KDA. The computer experiments show that the both criteria work well to select features but the former is more stable.