Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

Local kernel regression score for selecting features of high-dimensional data

Language

English

Abstract

In general, irrelevant features of high-dimensional data will degrade the performance of an inference system, e.g., a clustering algorithm or a classifier. In this paper, we therefore present a Local Kernel Regression (LKR) scoring approach to evaluate the relevancy of features based on their capabilities of keeping the local configuration in a small patch of data. Accordingly, a score index featuring applicability to both of supervised learning and unsupervised learning is developed to identify the relevant features within the framework of local kernel regression. Experimental results show the efficacy of the proposed approach in comparison with the existing methods. © 2006 IEEE.

Keywords

Feature selection, High-dimensional data, Local kernel regression score, Relevant features

Publication Date

2009

Source Publication Title

IEEE Transactions on Knowledge and Data Engineering

Volume

21

Issue

12

Start Page

1798

End Page

1802

Publisher

Institute of Electrical and Electronics Engineers

DOI

10.1109/TKDE.2009.23

Link to Publisher's Edition

http://dx.doi.org/10.1109/TKDE.2009.23

ISSN (print)

10414347

ISSN (electronic)

15582191

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