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A novel robust principal component analysis method for image and video processing

Guoqiang Huan, Ying Li, Zhanjie Song (2016)

Applications of Mathematics

The research on the robust principal component analysis has been attracting much attention recently. Generally, the model assumes sparse noise and characterizes the error term by the 1 -norm. However, the sparse noise has clustering effect in practice so using a certain p -norm simply is not appropriate for modeling. In this paper, we propose a novel method based on sparse Bayesian learning principles and Markov random fields. The method is proved to be very effective for low-rank matrix recovery...

A stochastic extension of R. Thomas regulatory network modelling

Bartek Wilczyński (2008)

Banach Center Publications

In this paper we present the extension of the kinetic logic proposed by René Thomas for analysis of genetic regulatory gene networks. We consider the case with a Gaussian noise added to the regulation function and propose a method of analyzing the resulting model with a discrete time Markov model.

Approximate Aggregation Methods in Discrete Time Stochastic Population Models

L. Sanz, J. A. Alonso (2010)

Mathematical Modelling of Natural Phenomena

Approximate aggregation techniques consist of introducing certain approximations that allow one to reduce a complex system involving many coupled variables obtaining a simpler ʽʽaggregated systemʼʼ governed by a few variables. Moreover, they give results that allow one to extract information about the complex original system in terms of the behavior of the reduced one. Often, the feature that allows one to carry out such a reduction is the presence...

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