潜在最小二乘回归子空间分割方法*

子空间分割已逐渐成为高维数据聚类的有效工具，但数据缺失或噪声干扰将直接影响子空间分割方法中仿射矩阵的构造，进而影响聚类效果.为解决这一问题，文中提出潜在最小二乘回归子空间分割方法，分别从行和列两个方向重构数据矩阵，并交替优化两个重构系数矩阵，充分考虑两个方向的表示信息.在6个基因表达数据集上的实验表明文中方法优于现有子空间分割方法.

Latent Least Square Regression for Subspace Segmentation

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.