Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing
Joint Authors
Li, Shuang
Zhang, Chen
Liu, Bing
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-05-09
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Li, Shuang& Liu, Bing& Zhang, Chen. 2016. Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1099691
Modern Language Association (MLA)
Li, Shuang…[et al.]. Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1099691
American Medical Association (AMA)
Li, Shuang& Liu, Bing& Zhang, Chen. Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1099691
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099691