A Survey on Statistical Pattern Feature Extraction

Authors:
Shifei Ding;Weikuan Jia;Chunyang Su;Fengxiang Jin;Zhongzhi Shi
Affiliations:
School of Computer Science and Technology, China University of Mining and, Technology, Xuzhou, 221008 and Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, C ...;College of Plant Protection, Shandong Agricultural University, Taian, 271018;School of Computer Science and Technology, China University of Mining and, Technology, Xuzhou, 221008;College of Geoinformation Science and Engineering, Shandong University of Science and Technology, Qingdao, 266510;Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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Abstract

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.