NDLI: Graph embedding based feature selection

Content Provider	IEEE Xplore Digital Library
Author	Dan Wei Shutao Li
Copyright Year	2011
Description	Author affiliation: College of Electrical and Information Engineering, Hunan University, Changsha, China (Dan Wei; Shutao Li)
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 and to eliminate the irrelevant feature 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 propose a recursive feature elimination (RFE) method using feature score for identifying the optimal feature subset. One advantage of the RFE method is that it can successfully identify the nonlinear features based on manifold learning. The experimental results both on face dataset and microarray dataset verify the effectiveness and efficiency of the proposed method.
Starting Page	1967
Ending Page	1971
File Size	146212
Page Count	5
File Format	PDF
ISBN	9781612841809
e-ISBN	9781612841816
DOI	10.1109/FSKD.2011.6019905
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Accuracy Feature selection Databases Noise Educational institutions Breast cancer Graph embedding Gene selection Manifold learning Face Recursive feature elimination
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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