Infinite Feature Selection

Roffo, G. , Melzi, S. and Cristani, M. (2015) Infinite Feature Selection. In: 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 07-13 Dec 2015, pp. 4202-4210. ISBN 9781467383912 (doi: 10.1109/ICCV.2015.478)

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Abstract

Filter-based feature selection has become crucial in many classification settings, especially object recognition, recently faced with feature learning strategies that originate thousands of cues. In this paper, we propose a feature selection method exploiting the convergence properties of power series of matrices, and introducing the concept of infinite feature selection (Inf-FS). Considering a selection of features as a path among feature distributions and letting these paths tend to an infinite number permits the investigation of the importance (relevance and redundancy) of a feature when injected into an arbitrary set of cues. Ranking the importance individuates candidate features, which turn out to be effective from a classification point of view, as proved by a thoroughly experimental section. The Inf-FS has been tested on thirteen diverse benchmarks, comparing against filters, embedded methods, and wrappers, in all the cases we achieve top performances, notably on the classification tasks of PASCAL VOC 2007-2012.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Roffo, Dr Giorgio
Authors: Roffo, G., Melzi, S., and Cristani, M.
College/School:College of Science and Engineering > School of Computing Science
ISSN:2380-7504
ISBN:9781467383912

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