TY - GEN
T1 - A training algorithm for sparse LS-SVM using compressive sampling
AU - Yang, Jie
AU - Bouzerdoum, Abdesselam
AU - Phung, Son Lam
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Compressive sampling
KW - Least Squares Support Vector Machine (LS-SVM)
KW - Model selection
KW - Orthogonal Matching Pursuit (OMP)
KW - Sparse approximation
UR - http://www.scopus.com/inward/record.url?scp=78049364222&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2010.5495015
DO - 10.1109/ICASSP.2010.5495015
M3 - Conference contribution
AN - SCOPUS:78049364222
SN - 9781424442966
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2054
EP - 2057
BT - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Y2 - 14 March 2010 through 19 March 2010
ER -