title: Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing.
abstract: Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios.
authors.affiliation: School of Management, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
authors.forename: Shuang
authors.initials: S
authors.lastname: Li
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
authors.forename: Chen
authors.initials: C
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D066264:Datasets as Topic; D006801:Humans; D010363:Pattern Recognition, Automated