Abstract
In this paper, a kernelized version of nonparametric discriminant analysis is proposed that we name KNDA. The main idea is to first map the original data into another high-dimensional space, and then to perform nonparametric discriminant analysis in the high dimensional space. Nonparametric discriminant analysis can relax the Gaussian assumption required for the classical linear discriminant analysis, and Kernel trick can further improve the separation ability. A group of tests on several UCI standard benchmarks have been carried out that prove our proposed method is very promising.
| Original language | English |
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| Title of host publication | 2011 International Conference on Electrical and Control Engineering, ICECE 2011 - Proceedings |
| Pages | 4544-4547 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2011 |
| Event | 2nd Annual Conference on Electrical and Control Engineering, ICECE 2011 - Yichang, China Duration: 16 Sept 2011 → 18 Sept 2011 |
Publication series
| Name | 2011 International Conference on Electrical and Control Engineering, ICECE 2011 - Proceedings |
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Conference
| Conference | 2nd Annual Conference on Electrical and Control Engineering, ICECE 2011 |
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| Country/Territory | China |
| City | Yichang |
| Period | 16/09/11 → 18/09/11 |
Keywords
- Kernel Linear Discriminant Analysis (KLDA)
- Kernel Nonparametric Discriminant Analysis (KNDA)
- Linear Discriminant Analysis (LDA)
- Nonparametric discriminant analysis
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Zhan, X., & Ma, B. (2011). Kernel nonparametric discriminant analysis. In 2011 International Conference on Electrical and Control Engineering, ICECE 2011 - Proceedings (pp. 4544-4547). Article 6057678 (2011 International Conference on Electrical and Control Engineering, ICECE 2011 - Proceedings). https://doi.org/10.1109/ICECENG.2011.6057678