Local Separability Assessment: A Novel Feature Selection Method for Multimedia Applications

Authors:
Kun Tao;Shou-Xun Lin;Yong-Dong Zhang;Sheng Tang
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100190 and Graduate School of the Chinese Academy of Sciences, Beijing, China 100049;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100190;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100190;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 100190
Venue:
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Year:
2008

Citing 3
Cited 0

Feature Selection for Knowledge Discovery and Data Mining

Feature Selection for Knowledge Discovery and Data Mining
An introduction to variable and feature selection

The Journal of Machine Learning Research
An Experimental Study on Feature Subset Selection Methods

CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Feature selection technology can help to reduce feature redundancy and improve classification performance. Most general feature selection methods do not perform well on high-dimension large-scale data sets of multimedia applications. In this paper we propose a novel feature selection method named Local Separability Assessment. We try to measure the separation level of samples in subregions of feature space, and integrate them for evaluating the separability of features. Our method has favorable performance on large-scale continuous data sets, and requires no priori hypothesis on data distribution. The experiments on various applications have proved its excellence.