A Survey on Statistical Pattern Feature Extraction

被引：0
|
作者：
Ding, Shifei [1 ,2 ]
Jia, Weikuan [3 ]
Su, Chunyang [1 ]
Jin, Fengxiang [4 ]
Shi, Zhongzhi [2 ]
机构：
[1] China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221008, Peoples R China
[2] Acad Sinica, Key Lab Intelligent Informat Proc, Inst Comop Technol, Beijing 100080, Peoples R China
[3] Agr Univ, Coll Plant Protection, Tai An, Shandong 271018, Peoples R China
[4] Shandong Univ Sci Technol, Coll Geoinformation Sci Engn, Qingdao 266510, Peoples R China
来源：
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2008年 / 5227卷
基金：
中国国家自然科学基金;
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The goal of statistical pattern feature extraction (SPFE) is 'low loss dimension reduction'. As the key link of pattern recognition, dimension reduction has become the research hot spot and difficulty in the fields of pattern recognition, machine learning, data mining and so on. Pattern feature extraction is one of the most challenging research fields and has attracted the attention from many scholars. This paper summarily introduces the basic principle of SPFE, and discusses the latest progress of SPFE from the aspects such as classical statistical theories and their modifications, kernel-based methods, wavelet analysis and its modifications, algorithms integration and so on. At last we discuss the development trend of SPFE.
引用
收藏
页码：701 / +
页数：3
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