MAHALANOBIS ELLIPSOIDAL LEARNING MACHINE FOR ONE CLASS CLASSIFICATION

摘要：

In this paper, we propose a novel kernel Mahalanobis ellipsoidal learning machine for one class classification.We propose to incorporate with the sample covariance matrix information and thus utilize the Mahalanobis distance rather than Euclidean distance in standard support vector data description.We use the centered kernel matrix and the singular value decomposition method to estimate the inverse of the sample covariance matrix.To avoid the existence of zero eigenvalues of the sample covariance matrix in high-dimensional feature space, we also introduce an uncertainty model to address a robust optimization problem.We investigate the initial performances of Mahalanobis ellipsoidal learning machine using the UCI benchmark datasets.

关键词： One class classification Mahalanobis distance Sample covariance matrix Singular value decomposition Reproducing kernel Hilbert space Robust optimization

作者: XUN-KAI WEI GUANG-BIN HUANG YING-HONG LI

作者单位: Dept.1, School of Engineering, Air Force Engineering University, Xian 710038, China School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue 63

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 3528-3533

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）