Title :
Sparse Kernel Regression Modelling Based on L1 Significant Vector Learning
Author :
Gao, Junbin ; Shi, Daming
Author_Institution :
Sch. of Inf. Technol., Charles Sturt Univ., Bathurst, NSW
Abstract :
A novel L1 significant vector (SV) regression algorithm is proposed in the paper. The proposed regularized L1 SV algorithm finds the significant vectors in a successive greedy process. The performance of the proposed algorithm is comparable to the OLS algorithm while it saves a lot of time complexities in implementing orthogonalization needed in the OLS algorithm
Keywords :
identification; least squares approximations; regression analysis; L1 significant vector learning; nonlinear system identification; orthogonal least squares algorithm; sparse kernel regression modelling; Kernel;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1615001
