Authors
Xinwang Liu, Lei Wang, Jian Zhang, Jianping Yin, Huan Liu
Publication date
2013/11/7
Journal
IEEE transactions on neural networks and learning systems
Volume
25
Issue
6
Pages
1083-1095
Publisher
IEEE
Description
The recent literature indicates that preserving global pairwise sample similarity is of great importance for feature selection and that many existing selection criteria essentially work in this way. In this paper, we argue that besides global pairwise sample similarity, the local geometric structure of data is also critical and that these two factors play different roles in different learning scenarios. In order to show this, we propose a global and local structure preservation framework for feature selection (GLSPFS) which integrates both global pairwise sample similarity and local geometric data structure to conduct feature selection. To demonstrate the generality of our framework, we employ methods that are well known in the literature to model the local geometric data structure and develop three specific GLSPFS-based feature selection algorithms. Also, we develop an efficient optimization algorithm with proven global …
Scholar articles
X Liu, L Wang, J Zhang, J Yin, H Liu - IEEE transactions on neural networks and learning …, 2013