Sparsity Based Feature Extraction for Kernel Minimum Squared Error

摘要：

　　Kernel minimum squared error(KMSE) is well-known for its effectiveness and simplicity,yet it suffers from the drawback of efficiency when the size of training examples is large.Besides,most of the previous fast algorithms based on KMSE only consider classification problems with balanced data,when in real world imbalanced data are common.In this paper,we propose a weighted model based on sparsity for feature selection in kernel minimum squared error(KMSE).With our model,the computational burden of feature extraction is largely alleviated.Moreover,this model can cope with the class imbalance problem.Experimental results conducted on several benchmark datasets indicate the effectivity and efficiency of our method.

关键词： Pattern classification Kernel MSE sparsity weighted,feature extraction

作者: Jiang Jiang Xi Chen Haitao Gan Nong Sang

作者单位: Science and Technology on Multi-spectral Information Processing Laboratory,School of Automation,Huaz Information and Telecommunication Branch of Hainan Power Grid,Hainan 570203,China School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 273-282

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）