A theoretical framework for multi-sphere support vector data description

Authors:
Trung Le;Dat Tran;Wanli Ma;Dharmendra Sharma
Affiliations:
Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia;Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia;Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia;Faculty of Information Sciences and Engineering, University of Canberra, ACT, Australia
Venue:
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Year:
2010

Citing 11
Cited 1

A probabilistic resource allocating network for novelty detection

Neural Computation
The nature of statistical learning theory

The nature of statistical learning theory
Support Vector Machines for Classification in Nonstandard Situations

Machine Learning
Local Expert Autoassociators for Anomaly Detection

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Data Description

Machine Learning
Consistency and Convergence Rates of One-Class SVMs and Related Algorithms

The Journal of Machine Learning Research
Statistical independence and novelty detection with information preserving nonlinear maps

Neural Computation
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass classification based on extended support vector data description

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multi-sphere Support Vector Data Description for Outliers Detection on Multi-distribution Data

ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
A new multi-class support vector machine with multi-sphere in the feature space

IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems

Deterministic annealing multi-sphere support vector data description

ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

In support vector data description (SVDD) a spherically shaped boundary around a normal data set is used to separate this set from abnormal data. The volume of this data description is minimized to reduce the chance of accepting abnormal data. However the SVDD does not guarantee that the single spherically shaped boundary can best describe the normal data set if there are some distinctive data distributions in this set. A better description is the use of multiple spheres, however there is currently no investigation available. In this paper, we propose a theoretical framework to multi-sphere SVDD in which an optimisation problem and an iterative algorithm are proposed to determine model parameters for multi-sphere SVDD to provide a better data description to the normal data set. We prove that the classification error will be reduced after each iteration in this learning process. Experimental results on 28 well-known data sets show that the proposed multisphere SVDD provides lower classification error rate comparing with the standard single-sphere SVDD.