Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing

• Main Authors: Shuang Li, Bing Liu, Chen Zhang
• Format: Article
• Language: English
• Published: Hindawi Limited 2016-01-01
• Series: Computational Intelligence and Neuroscience
• Online Access: http://dx.doi.org/10.1155/2016/4920670

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.