文章基本信息

标题：Neighborhood Component Feature Selection for High-Dimensional Data
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作者：Yang, Wei ; Wang, Kuanquan ; Zuo, Wangmeng 等
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2012
卷号：7
期号：1
页码：161-168
DOI：10.4304/jcp.7.1.161-168
语种：English
出版社：Academy Publisher
摘要：Feature selection is of considerable importance in data mining and machine learning, especially for high dimensional data. In this paper, we propose a novel nearest neighbor-based feature weighting algorithm, which learns a feature weighting vector by maximizing the expected leave-one-out classification accuracy with a regularization term. The algorithm makes no parametric assumptions about the distribution of the data and scales naturally to multiclass problems. Experiments conducted on artificial and real data sets demonstrate that the proposed algorithm is largely insensitive to the increase in the number of irrelevant features and performs better than the state-of-the-art methods in most cases.
关键词：feature selection; feature weighting; nearest neighbor