基于稀疏表示和最小二乘回归的基因表达数据分类方法
Gene expression data classification model based on sparse representation and least square regression

提出基于稀疏表示和最小二乘回归的分类方法： 用训练样本重构测试样本，先利用稀疏表示剔除噪声样本，接着用最小二乘回归和最近邻子空间准则对样本分类，可以克服传统分类方法存在的过拟合问题. 在6个基因表达数据上的实验结果表明，该方法可以提高分类准确率.
A new classification method based on sparse representation and least square regression is proposed. The method first reconstructs a test sample by the training samples to remove noise samples by using sparse representation，and then it uses least square regression and subspace nearest rule to classify samples. The method can overcome the over fitting problem of the traditional classification methods. Experimental results on six gene expression data sets show that the proposed method can improve the classification accuracy