Department of Computer Science
Local kernel regression score for selecting features of high-dimensional data
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.
Feature selection, High-dimensional data, Local kernel regression score, Relevant features
Source Publication Title
IEEE Transactions on Knowledge and Data Engineering
Institute of Electrical and Electronics Engineers
Link to Publisher's Edition
Cheung, Yiu-Ming, and Hong Zeng. "Local kernel regression score for selecting features of high-dimensional data." IEEE Transactions on Knowledge and Data Engineering 21.12 (2009): 1798-1802.