Probabilistic model for minor component analysis based on born rule

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 realiz...

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Main Authors: Jankovic, M. V., Manic, M., Relijn, B. D.
Format: Conference Proceeding
Language:English
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Born rule
Decision support systems
Minor component analysis
parallel hardware
time-oriented hierarchical learning
Tin
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Manic, M.
Relijn, B. D.
description 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.
doi_str_mv 10.1109/NEUREL.2012.6419971
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subjects Born rule
Decision support systems
Minor component analysis
parallel hardware
time-oriented hierarchical learning
Tin
title Probabilistic model for minor component analysis based on born rule
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