DocumentCode :
1808278
Title :
An information theoretic method for designing multiresolution principal component transforms
Author :
Jahromi, Omid S. ; Francis, Bruce A.
Author_Institution :
Dept. of Electr. & Comput. Eng., Toronto Univ., Ont., Canada
Volume :
2
fYear :
1999
fDate :
36342
Firstpage :
921
Abstract :
In signal processing, multiresolution transforms are used to decompose a time signal into components of different resolutions. In this paper, we consider designing optimal multiresolution transforms such that components in each resolution provide the best approximation to the original signal in that resolution. We call a transformation that admits this optimality property a principal component multiresolution transform (PCMT). We show that PCMTs can be designed by minimizing the information transfer through their basic building blocks. We then propose a method to do the minimization in a stage-by-stage manner. This latter method has a great appeal in terms of its computational simplicity as well as theoretical interpretations. In particular, it agrees with Linsker´s principle of self organization. Finally, we provide analytic arguments and computer simulations to demonstrate the efficiency of our method
Keywords :
information theory; minimisation; principal component analysis; signal processing; stochastic processes; transforms; Linsker principle; information theory; minimization; principal component analysis; principal component multiresolution transform; signal processing; stochastic process; Data compression; Design methodology; Filter bank; Information theory; Optimal control; Principal component analysis; Random variables; Signal processing; Signal resolution; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-5529-6
Type :
conf
DOI :
10.1109/IJCNN.1999.831076
Filename :
831076
Link To Document :
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