Infinite Feature Selection

Giorgio Roffo, Simone Melzi, Marco Cristani; The IEEE International Conference on Computer Vision (ICCV), 2015, pp. 4202-4210

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.

Related Material


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[bibtex]
@InProceedings{Roffo_2015_ICCV,
author = {Roffo, Giorgio and Melzi, Simone and Cristani, Marco},
title = {Infinite Feature Selection},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}