Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10361294 | Pattern Recognition | 2015 | 9 Pages |
Abstract
In this paper, a nonparametric classification technique which generalizes discriminant analysis has been proposed. The method of cross-validation is used to make the technique adaptive to a given dataset. An extensive simulation study is presented to illustrate the potential of the method. Finally, through implementation on a number of real-life data sets, it has been demonstrated that the proposed generalized quadratic discriminant analysis (GQDA) compares very favourably with other nonparametric methods, and is computationally cost-effective.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Smarajit Bose, Amita Pal, Rita SahaRay, Jitadeepa Nayak,