Abstract：Subspace segmentation is an efficient tool in high dimensional data clustering. However, the construction of affine matrix and the clustering result are directly affected by missing data and noise data. To solve this problem, latent least square regression for subspace segmentation (LatLSR) is proposed. The data matrix is reconstructed in directions of column and row, respectively. Two re-constructed coefficient matrices are optimized alternately, and thus the information in two directions is fully considered. The experimental results on six gene expression datasets show that the proposed method produces better performance than the existing subspace segmentation methods.
作者简介: 陈晓云(通讯作者),女，1970年生，博士，教授，主要研究方向为数据挖掘、模式识别等.Email:c_xiaoyun@21cn.com.Chen Xiaoyun (Corresponding author), born in 1970, Ph.D., professor. Her research interests include data mining, pattern recognition and machine learning, etc.)陈慧娟,女，1990年生，硕士研究生，主要研究方向为数据挖掘、模式识别.Email:446498859@qq.com.Chen Huijuan, born in 1990, master student. Her research interests include data mining and pattern recognition.)
2016, Vol. 29 
