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

Yiu-ming Cheung*, Hong Zeng

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

38 Citations (Scopus)
13 Downloads (Pure)


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.

Original languageEnglish
Article number4752826
Pages (from-to)1798-1802
Number of pages5
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number12
Publication statusPublished - Dec 2009

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • Relevant features
  • feature selection
  • local kernel regression score
  • high-dimensional data


Dive into the research topics of 'Local kernel regression score for selecting features of high-dimensional data'. Together they form a unique fingerprint.

Cite this