期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 21, 期 12, 页码 1798-1802出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2009.23
关键词
Relevant features; feature selection; local kernel regression score; high-dimensional data
类别
资金
- Research Grant Council of the Hong Kong SAR [HKBU 210306]
- Hong Kong Baptist University [FRG/07-08/11-54]
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.
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