Sparse Kernel ridge regression using backward deletion

Authors:
Ling Wang;Liefeng Bo;Licheng Jiao
Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, China
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

Citing 7
Cited 3

The nature of statistical learning theory

The nature of statistical learning theory
Machine learning, neural and statistical classification

Machine learning, neural and statistical classification
Bayesian Learning for Neural Networks

Bayesian Learning for Neural Networks
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Ridge Regression Learning Algorithm in Dual Variables

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Feature Scaling for Kernel Fisher Discriminant Analysis Using Leave-One-Out Cross Validation

Neural Computation
Sparse gaussian processes using backward elimination

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I

Optimally regularised kernel Fisher discriminant classification

Neural Networks
Reducing SVR Support Vectors by Using Backward Deletion

KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
A fast method of feature extraction for kernel MSE

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Based on the feature map principle, Sparse Kernel Ridge Regression (SKRR) model is proposed. SKRR obtains the sparseness by backward deletion feature selection procedure that recursively removes the feature with the smallest leave-one-out score until the stop criterion is satisfied. Besides good generalization performance, the most compelling property of SKRR is rather sparse, and moreover, the kernel function needs not to be positive definite. Experiments on synthetic and benchmark data sets validate the feasibility and validity of SKRR.