A Novel Sparse Least Squares Support Vector Machines

作者：Xia Xiao Lei^*; Jiao Weidong; Li Kang; Irwin George

来源：Mathematical Problems in Engineering, 2013, 2013: 602341.

DOI：10.1155/2013/602341

The solution of a Least Squares Support Vector Machine (LS-SVM) suffers from the problem of nonsparseness. The Forward Least Squares Approximation (FLSA) is a greedy approximation algorithm with a least-squares loss function. This paper proposes a new Support Vector Machine for which the FLSA is the training algorithm-the Forward Least Squares Approximation SVM (FLSA-SVM). A major novelty of this new FLSA-SVM is that the number of support vectors is the regularization parameter for tuning the tradeoff between the generalization ability and the training cost. The FLSA-SVMs can also detect the linear dependencies in vectors of the input Gramian matrix. These attributes together contribute to its extreme sparseness. Experiments on benchmark datasets are presented which show that, compared to various SVM algorithms, the FLSA-SVM is extremely compact, while maintaining a competitive generalization ability.

• 出版日期2013
• 单位浙江师范大学; 嘉兴学院