Probabilistic model for minor component analysis based on born rule
Marko V. Jankovic, Milos Manic, Branimir D. Relijn · 2012
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.