Please use this identifier to cite or link to this item:
Title: Graph embedding based feature selection
Authors: Wei, Dan.
Li, Shutao.
Tan, Mingkui.
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Wei, D., Li, S., & Tan, M. (2012). Graph embedding based feature selection. Neurocomputing, 93, 115-125.
Series/Report no.: Neurocomputing
Abstract: Usually many real datasets in pattern recognition applications contain a large quantity of noisy and redundant features that are irrelevant to the intrinsic characteristics of the dataset. The irrelevant features may seriously deteriorate the learning performance. Hence feature selection which aims to select the most informative features from the original dataset plays an important role in data mining, image recognition and microarray data analysis. In this paper, we developed a new feature selection technique based on the recently developed graph embedding framework for manifold learning. We first show that the recently developed feature scores such as Linear Discriminant Analysis score and Marginal Fisher Analysis score can be seen as a direct application of the graph preserving criterion. And then, we investigate the negative influence brought by the large noise features and propose two recursive feature elimination (RFE) methods based on feature score and subset level score, respectively, for identifying the optimal feature subset. The experimental results both on toy dataset and real-world dataset verify the effectiveness and efficiency of the proposed methods.
DOI: 10.1016/j.neucom.2012.03.016
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles


checked on Jul 16, 2020

Citations 50

checked on Oct 14, 2020

Page view(s) 50

checked on Oct 19, 2020

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.