Marko Filipovic

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BACKGROUND Bioinformatics data analysis is often using linear mixture model representing samples as additive mixture of components. Properly constrained blind matrix factorization methods extract those components using mixture samples only. However, automatic selection of extracted components to be retained for classification analysis remains an open issue.(More)
We propose a method for signal recovery in compressed sensing when measurements can be highly corrupted. It is based on minimization for. Since it was shown that minimization performs better than minimization when there are no large errors, the proposed approach is a natural extension to compressed sensing with corruptions. We provide a theoretical(More)
b Nonlinear underdetermined blind separation of nonnegative dependent sources consists in decomposing a set of observed nonlinearly mixed signals into a greater number of original nonnegative and dependent component (source) signals. This hard problem is practically relevant for contemporary metabolic profiling of biological samples, where sources (a.k.a.(More)
A mass balance was assembled for perfluorohexanoic acid (PFHxA), perfluorooctanoic acid (PFOA), perfluorodecanoic acid (PFDA), and perfluorooctanesulfonic acid (PFOS) in the Baltic Sea. Inputs (from riverine discharge, atmospheric deposition, coastal wastewater discharges, and the North Sea) and outputs (to sediment burial, transformation of the chemical,(More)
A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of(More)
We address the problem of restoration of images which have been affected by impulse or a combination of impulse and Gaussian noise. We propose a patch-based approach that exploits approximate sparse representations of image patches in learned dictionaries. For every patch, sparse representation in a dictionary is enforced by 1-norm penalty, and sparsity of(More)
Image inpainting consists in recovering missing parts of an image. Since a color image is a D array, tensor completion methods are applicable to this problem. Tensor completion approach based on trace norm minimization can be useful when the fraction of missing pixels is not large, with the advantage that the training set is not required. Here, we(More)
In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t Several supervised feature extraction methods for tensor objects have(More)