Compressive Classification of Sparse Signal with Support Vector Machine

Author

He Wei ; Li Yuebo ; Liu Feng

Author_Institution

Third Eng. Res. Inst. of the Headquarters of the Gen. Staff of PLA, Luoyang, China

Volume

1

fYear

2010

fDate

11-12 May 2010

Firstpage

998

Lastpage

1001

Abstract

Combining support vector machine (SVM) with compressive sensing (CS), a new classifier with compressive features is proposed. Based on this compressive classifier, a new method of classification is presented for the sparse modulated signals of 2FSK and 2ASK. Simulation results demonstrate that the performance of compressive classifier is close to that of traditional support vector classifier (SVC) with a significantly lower data requirement.

Keywords

amplitude shift keying; frequency shift keying; signal classification; support vector machines; compressive classification; compressive sensing; sparse modulated signals; support vector classifier; support vector machine; Automation; Helium; Linear approximation; Machine intelligence; Pattern classification; Pattern recognition; Programmable logic arrays; Static VAr compensators; Support vector machine classification; Support vector machines; compressive sensing; signal classification; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on

Conference_Location

Changsha

Print_ISBN

978-1-4244-7279-6

Electronic_ISBN

978-1-4244-7280-2

Type

conf

DOI

10.1109/ICICTA.2010.431

Filename

5522665