Abstract：The traditional clustering methods are inefficient due to high dimension and redundancy, small sample size and noise of the gene expression data. Subspace segmentation is an effective method for high dimensional data clustering. However, the performance of clustering is reduced by using subspace segmentation on the gene expression data directly. To cluster the gene expression data more effectively, low rank projection least square regression subspace segmentation method(LPLSR) is proposed. The improved low rank method is utilized to project gene expression data into the latent subspace to remove the possible corruptions in data and get a relatively clean data dictionary. Then, least square regression method is employed to obtain the low-dimension representation for data vectors and the affinity matrix is constructed to cluster the gene data. The experimental results on six public gene expression datasets show the validity of the proposed method.
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2017, Vol. 30 
