基于线性无关度提出了一种在高维特征空间中选择近似基的方法，并采用不完全抛弃法，充分利用非支持向量中的信息来建立稀疏最小二乘支持向量回归机的数学模型。另外，采用递推法加快了其模型的建立。该模型在保持预测精度基本不变的情况下，使支持向量的数目大大减少。最后，通过3个UCI数据集验证了该模型的有效性。

A novel method which selects the approximate bases of high dimensional feature space based on the scale of linear independency is proposed；and after combining the presented method with the partial reduction strategy,SLS-SVRM(Sparse Least Squares Support Vector Regression Machine) is built. In addition, the recursive trick is used to accelerate the establishment of SLS-SVRM. SLS-SVRM obviously decreases the number of support vector without loss of the predicted accuracy. Finally, three UCI (university of California at irvine) datasets confirm the effectiveness of the proposed model.