DocumentCode :
3071039
Title :
Probabilistic model for minor component analysis based on born rule
Author :
Jankovic, Marko V. ; Manic, Milos ; Relijn, B.D.
Author_Institution :
Electr. Eng. Inst. “Nikola Tesla”, Univ. of Belgrade, Belgrade, Serbia
fYear :
2012
fDate :
20-22 Sept. 2012
Firstpage :
85
Lastpage :
88
Abstract :
Minor component analysis (MCA) is commonly applied technique for data analysis and processing, e.g. compression or clustering. In this paper we propose a probabilistic MCA model based on the Born rule. In off-line realization it can be seen as a successive optimization problem. In the on-line realization it will be solved by introduction of two different time scales. It will be shown that recently proposed time oriented hierarchical method, can be used as a concept for on-line realization of the proposed algorithms. The proposed model gives general framework for creating different MCA realizations/algorithms. A particular realization can optimize locality of calculation, convergence speed, preciseness or some other parameter of interest.
Keywords :
data analysis; data compression; optimisation; pattern clustering; probability; MCA; born rule; data analysis; data clustering; data compression; data processing; minor component analysis; optimization problem; probabilistic model; Decision support systems; Tin; Born rule; Minor component analysis; parallel hardware; time-oriented hierarchical learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Network Applications in Electrical Engineering (NEUREL), 2012 11th Symposium on
Conference_Location :
Belgrade
Print_ISBN :
978-1-4673-1569-2
Type :
conf
DOI :
10.1109/NEUREL.2012.6419971
Filename :
6419971
Link To Document :
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