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Proceedings Paper

Discriminant power analyses of non-linear dimension expansion methods
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Paper Abstract

Most non-linear classification methods can be viewed as non-linear dimension expansion methods followed by a linear classifier. For example, the support vector machine (SVM) expands the dimensions of the original data using various kernels and classifies the data in the expanded data space using a linear SVM. In case of extreme learning machines or neural networks, the dimensions are expanded by hidden neurons and the final layer represents the linear classification. In this paper, we analyze the discriminant powers of various non-linear classifiers. Some analyses of the discriminating powers of non-linear dimension expansion methods are presented along with a suggestion of how to improve separability in non-linear classifiers.

Paper Details

Date Published: 19 May 2016
PDF: 6 pages
Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 98740O (19 May 2016); doi: 10.1117/12.2224454
Show Author Affiliations
Seongyoun Woo, Yonsei Univ. (Korea, Republic of)
Chulhee Lee, Yonsei Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 9874:
Remotely Sensed Data Compression, Communications, and Processing XII
Bormin Huang; Chein-I Chang; Chulhee Lee, Editor(s)

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