Title :
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
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;
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
DOI :
10.1109/ICICTA.2010.431
