Discriminant power analyses of non-linear dimension expansion methods

Seongyoun Woo, Chulhee Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationRemotely Sensed Data Compression, Communications, and Processing XII
PublisherSPIE
Volume9874
ISBN (Electronic)9781510601154
DOIs
Publication statusPublished - 2016 Jan 1
EventRemotely Sensed Data Compression, Communications, and Processing XII - Baltimore, United States
Duration: 2016 Apr 202016 Apr 21

Other

OtherRemotely Sensed Data Compression, Communications, and Processing XII
CountryUnited States
CityBaltimore
Period16/4/2016/4/21

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All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Woo, S., & Lee, C. (2016). Discriminant power analyses of non-linear dimension expansion methods. In Remotely Sensed Data Compression, Communications, and Processing XII (Vol. 9874). [98740O] SPIE. https://doi.org/10.1117/12.2224454