Optimal Locality Regularized Least Squares Support Vector Machine via Alternating Optimization

Authors:
Xiaobo Chen;Jian Yang;Jun Liang
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, People's Republic of China 210094 and School of Computer Science and Telecommunication Engineering ...;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, People's Republic of China 210094;School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, People's Republic of China 212013
Venue:
Neural Processing Letters
Year:
2011

Citing 13
Cited 2

The nature of statistical learning theory

The nature of statistical learning theory
Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
Making large-scale support vector machine learning practical

Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
Least Squares Support Vector Machine Classifiers

Neural Processing Letters
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
Rule-Based Learning Systems for Support Vector Machines

Neural Processing Letters
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

The Journal of Machine Learning Research
Structured large margin machines: sensitive to data distributions

Machine Learning
2008 Special Issue: Low rank updated LS-SVM classifiers for fast variable selection

Neural Networks
Structural Support Vector Machine

ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Graph-optimized locality preserving projections

Pattern Recognition

Recursive robust least squares support vector regression based on maximum correntropy criterion

Neurocomputing
Regularized least squares fisher linear discriminant with applications to image recognition

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The least squares support vector machine (LSSVM), like standard support vector machine (SVM) which is based on structural risk minimization, can be obtained by solving a simpler optimization problem than that in SVM. However, local structure information of data samples, especially intrinsic manifold structure, is not taken full consideration in LSSVM. To address this problem and inspired by manifold learning technique, we propose a novel iterative least squares classifier, coined optimal locality preserving least squares support vector machine (OLP-LSSVM). The idea is to combine structural risk minimization and locality preserving criterion in a unified framework to take advantage of the manifold structure of data samples to enhance LSSVM. Furthermore, inspired by the recent development of simultaneous optimization technique, adjacent graph of locality preserving criterion is optimized simultaneously to give rise to improved discriminative performance. The resulting model can be solved by alternating optimization method. The experimental results on several publicly available benchmark data sets show the feasibility and effectiveness of the proposed method.