Dimensionality reduction method based on minimum classification error and Parzen window

Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (14): 185-188.

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Dimensionality reduction method based on minimum classification error and Parzen window

HE Dengchao, HAO Wenning, CHEN Gang, JIN Dawei

Engineering Institute of Corps of Engineers, PLA University of Science & Technology, Nanjing 210007, China

Online:2014-07-15 Published:2014-08-04

基于最小分类错误率和Parzen窗的降维方法

贺邓超，郝文宁，陈刚，靳大尉

解放军理工大学工程兵工程学院，南京 210007

Abstract

Abstract: A dimensionality reduction method based on minimum classification error and Parzen window is proposed, which firstly uses Parzen window to estimate the probability density of data, then calculates the contribution for classification of each feature dimension with the classification error, and selects the feature dimension according to the contribution for classification, in such a way as to achieve the intention of dimensionality reduction.

Key words: Parzen window, dimensionality reduction, density probability, feature selection

摘要： 提出了一种基于最小分类错误率和Parzen窗的降维方法，利用Parzen窗估计数据的概率密度分布;通过计算各特征维度下的分类错误率，判断该特征维度对目标分类的贡献度;依据贡献度大小进行特征维度选择从而达到降维的目的。

关键词: Parzen窗, 降维, 概率密度, 特征选择

HE Dengchao, HAO Wenning, CHEN Gang, JIN Dawei. Dimensionality reduction method based on minimum classification error and Parzen window[J]. Computer Engineering and Applications, 2014, 50(14): 185-188.

贺邓超，郝文宁，陈刚，靳大尉. 基于最小分类错误率和Parzen窗的降维方法[J]. 计算机工程与应用, 2014, 50(14): 185-188.

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Dimensionality reduction method based on minimum classification error and Parzen window

基于最小分类错误率和Parzen窗的降维方法

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