Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/53584
Title: | Approximate polytope ensemble for one-class classification |
Author: | Casale, Pierluigi Pujol Vila, Oriol Radeva, Petia |
Keywords: | Algorismes computacionals Processament digital d'imatges Geometria convexa Computer algorithms Digital image processing Convex geometry |
Issue Date: | Feb-2014 |
Publisher: | Elsevier Ltd |
Abstract: | In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets. |
Note: | Versió postprint del document publicat a: http://dx.doi.org/10.1016/j.patcog.2013.08.007 |
It is part of: | Pattern Recognition, 2014, vol. 47, num. 2, p. 854-864 |
URI: | http://hdl.handle.net/2445/53584 |
Related resource: | http://dx.doi.org/10.1016/j.patcog.2013.08.007 |
ISSN: | 0031-3203 |
Appears in Collections: | Articles publicats en revistes (Matemàtiques i Informàtica) Publicacions de projectes de recerca finançats per la UE |
Files in This Item:
File | Description | Size | Format | |
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638858.pdf | 1.25 MB | Adobe PDF | View/Open |
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