A training algorithm for sparse LS-SVM using compressive sampling

Authors

Jie Yang, University of WollongongFollow
Son Lam Phung, University of WollongongFollow
Abdesselam Bouzerdoum, University of WollongongFollow

RIS ID

33603

Publication Details

Yang, J., Bouzerdoum, A. & Phung, S. (2010). A training algorithm for sparse LS-SVM using compressive sampling. 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 2054-2057). USA: IEEE.

Abstract

Least Squares Support Vector Machine (LS-SVM) has become a fundamental tool in pattern recognition and machine learning. However, the main disadvantage is lack of sparseness of solutions. In this article Compressive Sampling (CS), which addresses the sparse signal representation, is employed to find the support vectors of LS-SVM. The main difference between our work and the existing techniques is that the proposed method can locate the sparse topology while training. In contrast, most of the traditional methods need to train the model before finding the sparse support vectors. An experimental comparison with the standard LS-SVM and existing algorithms is given for function approximation and classification problems. The results show that the proposed method achieves comparable performance with typically a much sparser model.