加权熵最大优化分带分析方法及在模式分类中的应用

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1074 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Power spectral subband analysis with the criterion of maximum weighting entropy is derived as a new signal analysis method in this paper. The maximum information is obtained by optimizing the subbands allocated in frequency. Based on this method, a algorithm of feature extraction for classification, maximum weighting entropy cepstrum coefficients (MECC), is proposed and applied to ground vehicle recognition system. Experimental results show that MECC has better classification performance than the traditional methods.

Key words： Maximum Weighting Entropy SubBand Analysis Genetic Algorithm

Received: 20 November 2006

ZTFLH:

TP391.42

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	BAO Ming
	GUAN LuYang
	LI XiaoDong
	TIAN Jing

Cite this article:

BAO Ming,GUAN LuYang,LI XiaoDong等. SubBand Optimization with Criterion of Maximum Weighting Entropy and Its Application in Pattern Classification[J]. , 2008, 21(1): 42-48.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2008/V21/I1/42

[1] Vaidyanathan P P. Theory and Design of MChannel Maximally Decimated Quadrature Mirror Filters with Arbitrary M, Having Perfect Reconstruction Property. IEEE Trans on Acoustics, Speech and Signal Processing, 1987, 35(4): 476496
[2] Mallat S G. A Theory for Multiresolution Signal Decomposition: the Wavelet Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674693
[3] Jayant N, Johnston J, Safranek R. Signal Compression Based on Method of Human Perception. Proc of the IEEE, 1993, 81(10): 13851422
[4] Coifman R R, Wickerhauser M V. Entropy Based Algorithm for Best Basis Selection. IEEE Trans on Information Theory, 1992, 38(2): 713718
[5]Jaynes E T. Information Theory and Statistical Mechanics, Physical Review, 1957, 106(4): 620630
[6] Kullback S. Information Theory and Statistics. New York, USA: John Wily&Sons, 1959
[7] Shore J E, Johnson R W. Axiomatic Derivation of the Principle of Maximum Entropy and the Principle of Minimum CrossEntropy. IEEE Trans on Information Theory, 1980, 26(1): 2637
[8] Bell A J, Sejnowski T J. An InformationMaximization Approach to Blind Separation and Blind Deconvolution. Neural Computation, 1995, 7(6): 11291159
[9] Erdogmus D, Principe J C. Comparison of Entropy and Mean Square Error Criteria in Adaptive System Training Using Higher Order Statistics // Proc of the Independent Components Analysis. Helsinki, Finland, 2000: 7580
[10] Erdogmus D, Principe J C. Generalized Information Potential Criterion for Adaptive System Training. IEEE Trans on Neural Networks, 2002, 13(5): 10351044
[11] Duda R O, Hart P E, Stork D G. Pattern Classification. 2nd Edition. New York, USA: Wiley, 2000
[12] Li D, Wong K D, Hu Y H, et al. Detection, Classification and Tracking of Targets in Distributed Sensor Networks. IEEE Signal Processing Magazine, 2002, 19(2): 1729
[13] Jiang Dan, Qian Yumei. Information Theory and Coding. Hefei, China: USTC Press, 1992 (in Chinese)
(姜丹,钱玉美. 信息理论与编码.合肥:中国科技大学出版社, 1992)