MKLasso模型的算法与模型选择

在KLasso模型基础之上,引入多核函数与多核参数重新建立的一种更为广发的非线性的多核KLasso模型（MKLasso模型）,采用基于梯度Boosting的思想的算法进行求解,并依据人类观察事物的一个基本特征,即人眼位于数据空间较近时能够看清细节,较远时只能够看清整体结构的特性设计了一种模型选择策略,通过实际的3个数据集设计6组试验,来验证该算法的有效性。模拟试验结果表明：MKLasso模型的预测能力明显优于KLasso模型,其预测均方误差提高了10倍;该算法运行高效,抗噪声能力强,在参数选择方面又有一定自己的优势,可以直接选择核参数,算法大大降低了调试与运算时间。

MKLasso model algorithm and model choice

A nonlinear Mutil-kernel Lasso model which is a generalization of classical KLasso was developed based on introducing the Mutil-kernel function and parameters.To solve MKLasso,an algorithm in terms of the gradient boosting perspective was designed.The algorithm can select kernel parameters directly,so it can reduce the complexity and running time.Moreover,in order to improve the performance of the algorithm,a new model selection strategy was proposed by one of the principles of human vision system that is person stands near the data space,they can see the clear local structure of the data,person stands far away the data space,then they only can see the clear global structure.The efficiency of the algorithm was proved by the 6 simulations designed by 3 real data set.Finally,a series of simulations indicate that the prediction capacity of MKLasso outperformance KLasso at least 10 times in terms of prediction mean square error.The algorithm is not only efficient and rubust,but also has flexible model selection strategy to select the kernel parameters directly,which can reduce the testing and running time.