Abstract Kernel algorithm is a new nonlinear technique in the machine learning field with great practical value. In the Kernel algorithm Kernel Matrix serves as a bridge between datain and learning algorithms. The lowrank approximation of Kernel Matrix is an effective means of advancing the computational efficiency of Kernel algorithm and reducing the internal memory. Based on the correlation between data and minimal residual norm, the forward greedy algorithm, the backward greedy algorithm and the mixed greedy algorithm are proposed to look for the optimum lowrank approximation. A sparse regression algorithm (SRA) is presented which effectively cuts off “training samples” and keeps good generalization capacity. With comparison to SVM,KA and KPCR, the superiority of SRA is demonstrated on two datasets.
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2007, Vol. 20 
