Graph embedding based feature selection

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...

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Main Authors: Wei, Dan., Li, Shutao., Tan, Mingkui.
Other Authors: School of Computer Engineering
Format: Article
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/84508
http://hdl.handle.net/10220/13651
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-845082020-05-28T07:17:49Z Graph embedding based feature selection Wei, Dan. Li, Shutao. Tan, Mingkui. School of Computer Engineering DRNTU::Engineering::Computer science and engineering 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. 2013-09-24T07:13:33Z 2019-12-06T15:46:15Z 2013-09-24T07:13:33Z 2019-12-06T15:46:15Z 2012 2012 Journal Article Wei, D., Li, S., & Tan, M. (2012). Graph embedding based feature selection. Neurocomputing, 93, 115-125. https://hdl.handle.net/10356/84508 http://hdl.handle.net/10220/13651 10.1016/j.neucom.2012.03.016 en Neurocomputing
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Wei, Dan.
Li, Shutao.
Tan, Mingkui.
Graph embedding based feature selection
description 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.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Wei, Dan.
Li, Shutao.
Tan, Mingkui.
format Article
author Wei, Dan.
Li, Shutao.
Tan, Mingkui.
author_sort Wei, Dan.
title Graph embedding based feature selection
title_short Graph embedding based feature selection
title_full Graph embedding based feature selection
title_fullStr Graph embedding based feature selection
title_full_unstemmed Graph embedding based feature selection
title_sort graph embedding based feature selection
publishDate 2013
url https://hdl.handle.net/10356/84508
http://hdl.handle.net/10220/13651
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