Multi-scale Kernel discrminant analysis

Ghosh, Anil Kumar ; Chaudhuri, Probal ; Sengupta, Debasis (2003) Multi-scale Kernel discrminant analysis Proceedings of the 5th International Conference on Advances in Pattern Recognition . pp. 89-93. ISSN 1017-0405

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The bandwidth that minimizes the mean integrated square error of a kernel density estimator may not always be good when the density estimate is used for classification purpose. On the other hand cross-validation based techniques for choosing bandwidths may not be computationally feasible when there are many competing classes. Instead of concentrating on a single optimum bandwidth for each population density estimate, it would be more useful in practice to look at the results for different scales of smoothing. This paper presents such a multi-scale approach for classification using kernel density estimates along with a graphical device that leads to a more informative discriminant analysis. Usefulness of this proposed methodology has been illustrated using some benchmark data sets.

Item Type:Article
Source:Copyright of this article belongs to Statistica Sinica.
ID Code:74636
Deposited On:17 Dec 2011 10:31
Last Modified:18 May 2016 18:58

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