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Multiple Kernel Dimensionality Reduction via Ratio-Trace and Marginal Fisher Analysis
Joint Authors
Xu, Hui
Yang, Yongguo
Wang, Xin
Liu, Mingming
Xie, Hongxia
Wang, Chujiao
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-14
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Traditional supervised multiple kernel learning (MKL) for dimensionality reduction is generally an extension of kernel discriminant analysis (KDA), which has some restrictive assumptions.
In addition, they generally are based on graph embedding framework.
A more general multiple kernel-based dimensionality reduction algorithm, called multiple kernel marginal Fisher analysis (MKL-MFA), is presented for supervised nonlinear dimensionality reduction combined with ratio-race optimization problem.
MKL-MFA aims at relaxing the restrictive assumption that the data of each class is of a Gaussian distribution and finding an appropriate convex combination of several base kernels.
To improve the efficiency of multiple kernel dimensionality reduction, the spectral regression frameworks are incorporated into the optimization model.
Furthermore, the optimal weights of predefined base kernels can be obtained by solving a different convex optimization.
Experimental results on benchmark datasets demonstrate that MKL-MFA outperforms the state-of-the-art supervised multiple kernel dimensionality reduction methods.
American Psychological Association (APA)
Xu, Hui& Yang, Yongguo& Wang, Xin& Liu, Mingming& Xie, Hongxia& Wang, Chujiao. 2019. Multiple Kernel Dimensionality Reduction via Ratio-Trace and Marginal Fisher Analysis. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1196752
Modern Language Association (MLA)
Xu, Hui…[et al.]. Multiple Kernel Dimensionality Reduction via Ratio-Trace and Marginal Fisher Analysis. Mathematical Problems in Engineering No. 2019 (2019), pp.1-8.
https://search.emarefa.net/detail/BIM-1196752
American Medical Association (AMA)
Xu, Hui& Yang, Yongguo& Wang, Xin& Liu, Mingming& Xie, Hongxia& Wang, Chujiao. Multiple Kernel Dimensionality Reduction via Ratio-Trace and Marginal Fisher Analysis. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-8.
https://search.emarefa.net/detail/BIM-1196752
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196752