电子学报2012，Vol.40Issue(1)：134-140,7.DOI:10.3969/j.issn.0372-2112.2012.01.022
基于L1范数稀疏距离测度学习的单类分类算法
L1 Norm Sparse Distance Metric Learning for One-Class Classifier
摘要
Abstract
Most one-class classification algorithms measure similarity based on Euclidean distance between samples.Unfortunately, the Euclidean distance couldn' t reveal the internal distribution of some datasets, and so reduced the descriptive ability of these methods. A distance metric learning algorithm was proposed to improve the performance of one-class classifiers in this paper. Compared with existing distance metric learning algorithm, the algorithm only needed to provide target class data, it could effectively solve distance metric learning problem for one-class samples in high-dimensional space by imposing sample distribution prior and sparsity prior with 11-norm constraint on the distance metric,and the formulation could be efficiently optimized in a block coordination descent algorithm.The learned metric can be easily embedded into one-class classifiers, the simulation experimental results show that the learned metric can effectively improve the description performance of one-class classifiers, in particular the description of covering classification model and obtain better generalization ability of one-class classifiers.关键词
模式识别/稀疏距离测度学习/L1范数/单类分类器Key words
pattern recognition/ sparse distance metric learning/ L1-norm/ one-class classifier分类
信息技术与安全科学引用本文复制引用
胡正平,路亮,许成谦..基于L1范数稀疏距离测度学习的单类分类算法[J].电子学报,2012,40(1):134-140,7.基金项目
国家自然科学基金(No.61071199) （No.61071199）
河北省自然科学基金(No.F2008000891,No.F2010001297) （No.F2008000891,No.F2010001297）
中国博士后自然科学基金(No.20080440124) （No.20080440124）
第二批中国博士后特别资助基金(No.200902356) （No.200902356）
