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.
2016, Vol. 29 
