计算机应用研究2013，Vol.30Issue(7)：2230-2232,3.DOI:10.3969/j.issn.1001-3695.2013.07.078
鲁棒的加权核主成分分析算法
Robust weighted KPCA algorithm
摘要
Abstract
This paper proposed a robust weighted KPCA to reduce the effect of outliers in data processing.By introducing kernel function to project samples into kernel space,it constructed a model minimizing weighted reconstruction errors in the kernel space,to maximize nonlinear information extracted from the data and reduce the interference of outlier samples.Experiments on Yale face database with outliers and UCI data sets show that the proposed method has better recognition rates and robustness especially with outliers.关键词
特征提取/人脸识别/核主成分分析/鲁棒Key words
feature extraction/face recongnition/KPCA(kernel principal component analysis)/robust分类
信息技术与安全科学引用本文复制引用
孟凡荣,杨开睿,梁志贞..鲁棒的加权核主成分分析算法[J].计算机应用研究,2013,30(7):2230-2232,3.基金项目
国家自然科学基金资助项目(61003169) （61003169）
国家教育部博士点基金资助项目(20110095110010) （20110095110010）
中央高校基本科研业务费专项基金资助项目(2012ANA33) （2012ANA33）
