基于压缩感知的多核稀疏最小二乘支持向量机

为了提高稀疏最小二乘支持向量机对高维、异构数据的泛化性能,提出新型的基于压缩感知的稀疏多核最小二乘支持向量机算法。首先根据压缩感知理论,用正交匹配追踪算法对最小二乘支持向量机的支持向量进行稀疏化,再利用线性多核扩展法求出新的核函数矩阵。将新的核矩阵应用到最小二乘支持向量机,得到稀疏多核最小二乘支持向量机的解,用稀疏的支持向量实现函数回归。理论分析与数据实验对比结果表明该模型对于高维、异构数据能够更快更准确地进行训练,大大提高了模型的泛化能力和运算速度。

Multi-kernal sparse least square support vector machine using compressive sensing

To improve the generalization performance of the sparse least square support vector machine for the high dimensional and heterogeneous data, a multi-kernel sparse least square support vector machine based on compressive sensing is presented. Firstly, according to the theory of compressive sensing, support vectors of the least square support vector machine are spared by the orthogonal matching pursuit algorithm. Then the new kernel matrix is calculated by the linear multi-kernel combination method. The solution of the sparse least square support vector machine is obtained by applying the new kernel matrix to the least square support vector machine. Finally, the function regression is achieved by using spares support vectors. The theoretical analyses and contract experiment results show the proposed algorithm has better performance and faster speed for high dimensional and heterogeneous data than those in the available, which greatly improves the generalization ability and operation speed of the algorithm.