A survey on statistical pattern feature extraction

9Citations
Citations of this article
22Readers
Mendeley users who have this article in their library.
Get full text

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. © 2008 Springer-Verlag Berlin Heidelberg.

Cite

CITATION STYLE

APA

Ding, S., Jia, W., Su, C., Jin, F., & Shi, Z. (2008). A survey on statistical pattern feature extraction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5227 LNAI, pp. 701–708). https://doi.org/10.1007/978-3-540-85984-0_84

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free