计算机工程与科学2017，Vol.39Issue(3)：553-561,9.DOI:10.3969/j.issn.1007-130X.2017.03.022
基于RPCA对高维数据子空间聚类的预测方法
A predictive subspace clustering method of high-dimensional data based on RPCA
摘要
Abstract
Because the predictive subspace clustering (PSC) algorithm is not robust to the principal component analysis in the PCA model,the clustering performance is severely affected when dealing with the data with strong noise.In order to improve the robustness to noise of the PSC algorithm,we use the robust principal component analysis (RPCA) decomposition technique which is paid extensive attention in recent years to obtain the low rank structure of the data and achieve a robust extraction subspace.We integrate the RPCA model into the PSC algorithm and propose a predictive subspace clustering algorithm based on the RPCA.The proposed algorithm can detect influential observations in the RPCA model,effectively carry out variable selection and model selection,and more importantly it can improve the clustering performance of the PSC algorithm in noise environment.Experimental results on real gene expression data sets show that the improved algorithm has clustering advantages and better robustness both in the noise environment and the environment without noise in comparison with the classical algorithm PSC.关键词
RPCA/子空间聚类/变量选择/模型选择/鲁棒性Key words
RPCA/subspace clustering/variable selection/model selection/robustness分类
信息技术与安全科学引用本文复制引用
吕红伟,王士同..基于RPCA对高维数据子空间聚类的预测方法[J].计算机工程与科学,2017,39(3):553-561,9.基金项目
国家自然科学基金(61272210) （61272210）
