Dimensionality Reduction by Supervised Neighbor Embedding Using Laplacian Search
Joint Authors
Wang, Wanliang
Zheng, Jianwei
Cattani, Carlo
Zhang, Hangke
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-05-21
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Dimensionality reduction is an important issue for numerous applications including biomedical images analysis and living system analysis.
Neighbor embedding, those representing the global and local structure as well as dealing with multiple manifolds, such as the elastic embedding techniques, can go beyond traditional dimensionality reduction methods and find better optima.
Nevertheless, existing neighbor embedding algorithms can not be directly applied in classification as suffering from several problems: (1) high computational complexity, (2) nonparametric mappings, and (3) lack of class labels information.
We propose a supervised neighbor embedding called discriminative elastic embedding (DEE) which integrates linear projection matrix and class labels into the final objective function.
In addition, we present the Laplacian search direction for fast convergence.
DEE is evaluated in three aspects: embedding visualization, training efficiency, and classification performance.
Experimental results on several benchmark databases present that the proposed DEE exhibits a supervised dimensionality reduction approach which not only has strong pattern revealing capability, but also brings computational advantages over standard gradient based methods.
American Psychological Association (APA)
Zheng, Jianwei& Zhang, Hangke& Cattani, Carlo& Wang, Wanliang. 2014. Dimensionality Reduction by Supervised Neighbor Embedding Using Laplacian Search. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-483626
Modern Language Association (MLA)
Zheng, Jianwei…[et al.]. Dimensionality Reduction by Supervised Neighbor Embedding Using Laplacian Search. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-14.
https://search.emarefa.net/detail/BIM-483626
American Medical Association (AMA)
Zheng, Jianwei& Zhang, Hangke& Cattani, Carlo& Wang, Wanliang. Dimensionality Reduction by Supervised Neighbor Embedding Using Laplacian Search. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-483626
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-483626