[1]刘忠宝,王士同.从Parzen窗核密度估计到特征提取方法：新的研究视角[J].智能系统学报,2012,7(06):471-480.
　LIU Zhongbao,WANG Shitong.From Parzen window estimation to feature extraction: a new perspective[J].CAAI Transactions on Intelligent Systems,2012,7(06):471-480.
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从Parzen窗核密度估计到特征提取方法：新的研究视角(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第7卷
期数:
2012年06期
页码:
471-480
栏目:
出版日期:
2012-12-25

文章信息/Info

Title:
From Parzen window estimation to feature extraction: a new perspective
文章编号:
1673-4785(2012)06-0471-10
作者:
刘忠宝1; 2; 王士同1
1. 江南大学 数字媒体学院，江苏 无锡 214122；
2. 中北大学 电子与计算机科学技术学院，山西 太原 030051
Author(s):
LIU Zhongbao1; 2;  WANG Shitong1
1. School of Digital Media, Jiangnan University, Wuxi 214122, China;
2. School of Electronics and Computer Science Technology, North University of China, Taiyuan 030051, China
关键词:
特征提取; Parzen窗; 密度估计; 数据分布特征; 新视角
Keywords:
feature extraction;  Parzen window;  density estimation;  data distribution characteristics;  new perspective
分类号:
TP392
文献标志码:
A
摘要:
当前主流特征提取方法大致有2种研究思路：1）从高维数据的几何性质出发，根据某种寻优准则得到基于原始空间特征的一组特征数更少的新特征；2）从降维误差角度出发，保证降维前后数据所呈现的某种偏差达到最小．试图从降维过程中数据分布特征的变化入手，基于广泛使用的Parzen窗核密度估计方法，来审视和揭示Parzen窗估计与典型特征提取方法LPP、LDA和PCA之间的关系，从而说明这些特征提取方法可统一在Parzen窗框架下进行研究，为特征提取方法的研究提供了一个新的视角．
Abstract:
Researches on current feature extraction methods are mainly based on two ways. One originates from geometric properties of highdimensional datasets and attempt to extract fewer features from the original data space according to a certain criterion. The other originates from dimension reduction deviation and try to make the deviation between data before and after dimension reduction be as small as possible. However, there exists almost no study about them from the perspective of the scatter change of a dataset. Based on Parzen window density estimator, we thoroughly revisit the relevant feature extraction methods from a new perspective and the relationships between Parzen window and classical feature extraction methods,ie length of perpendiculars (LPP), linear discriminant analysis (LDA) and principal component analysis (PCA) are built in this paper. Therefore, these feature extraction methods can be researched in the same Parzen window, which provides a new perspective for the research of feature extraction.

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备注/Memo

备注/Memo:
收稿日期：2012-05-14.
网络出版日期：2012-11-16.
基金项目：国家自然科学基金资助项目(61170122, 61272210). 
通信作者：刘忠宝.
E-mail：liu_zhongbao@hotmail.com.
作者简介：
刘忠宝，男，1981年生，博士，主要研究方向为模式识别、机器学习，发表学术论文数十篇．
王士同，男，1964年生，教授，博士生导师，主要研究方向为人工智能与机器学习．
更新日期/Last Update: 2013-03-19