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 language | English |
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| Title of host publication | Remotely Sensed Data Compression, Communications, and Processing XII |
| Editors | Chulhee Lee, Bormin Huang, Chein-I Chang |
| Publisher | SPIE |
| ISBN (Electronic) | 9781510601154 |
| DOIs | |
| Publication status | Published - 2016 |
| Event | Remotely Sensed Data Compression, Communications, and Processing XII - Baltimore, United States Duration: 2016 Apr 20 → 2016 Apr 21 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 9874 |
| ISSN (Print) | 0277-786X |
| ISSN (Electronic) | 1996-756X |
Other
| Other | Remotely Sensed Data Compression, Communications, and Processing XII |
|---|---|
| Country/Territory | United States |
| City | Baltimore |
| Period | 16/4/20 → 16/4/21 |
Bibliographical note
Publisher Copyright:© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering