模式收缩最小二乘回归子空间分割

基因表达数据聚类为肿瘤新类别的发现提供了重要手段。然而,直接对原始数据进行聚类会在一定程度上丢失数据本身隐含的流形结构信息,影响子空间分割方法的聚类效果。为解决这一问题,提出模式收缩最小二乘回归(pattern shrinking least square regression, PSLSR)子空间分割方法。该模型能够同时进行模式收缩和仿射矩阵的学习,并利用交替优化方法进行求解。在6个基因表达数据上的实验结果表明该方法优于现有子空间分割方法。

Pattern shrinking least square regression for subspace segmentation

Clustering of gene expression data is an important method to discover the new category of tumor. However, clustering directly on the original gene data will lose the hidden manifold structure information, and then affect the clustering effect of the subspace segmentation method. In order to solve this problem, the pattern shrinking least square regression model for subspace segmentation(PSLSR)is proposed. This model can perform pattern shrinking and learn the affine matrix of data simultaneously, and be solved by using the alternating optimization method. Experimental results on six gene expression data show that PSLSR significantly outperforms the existing subspace segmentation methods.