title: Training Lp norm multiple kernel learning in the primal.
abstract: Some multiple kernel learning (MKL) models are usually solved by utilizing the alternating optimization method where one alternately solves SVMs in the dual and updates kernel weights. Since the dual and primal optimization can achieve the same aim, it is valuable in exploring how to perform Lp norm MKL in the primal. In this paper, we propose an Lp norm multiple kernel learning algorithm in the primal where we resort to the alternating optimization method: one cycle for solving SVMs in the primal by using the preconditioned conjugate gradient method and other cycle for learning the kernel weights. It is interesting to note that the kernel weights in our method can obtain analytical solutions. Most importantly, the proposed method is well suited for the manifold regularization framework in the primal since solving LapSVMs in the primal is much more effective than solving LapSVMs in the dual. In addition, we also carry out theoretical analysis for multiple kernel learning in the primal in terms of the empirical Rademacher complexity. It is found that optimizing the empirical Rademacher complexity may obtain a type of kernel weights. The experiments on some datasets are carried out to demonstrate the feasibility and effectiveness of the proposed method.
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, China. cuhk_liang@yahoo.cn
authors.forename: Zhizheng
authors.initials: Z
authors.lastname: Liang
authors.forename: Shixiong
authors.initials: S
authors.lastname: Xia
authors.forename: Yong
authors.initials: Y
authors.lastname: Zhou
authors.forename: Lei
authors.initials: L
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*; D013223:Statistics as Topic
keywords: Data classification; Empirical Rademacher complexity; Manifold regularization; Multiple kernel learning; Primal optimization