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pro vyhledávání: '"DE LATHAUWER, LIEVEN"'
Nonnegative Matrix Factorization (NMF) models are widely used to recover linearly mixed nonnegative data. When the data is made of samplings of continuous signals, the factors in NMF can be constrained to be samples of nonnegative rational functions,
Externí odkaz:
http://arxiv.org/abs/2209.12579
Autor:
Ashtari, Pooya, Sima, Diana M., De Lathauwer, Lieven, Sappey-Marinier, Dominique, Maes, Frederik, Van Huffel, Sabine
Publikováno v:
Medical Image Analysis 84 (2023) 102706
Convolutional Neural Networks (CNNs) with U-shaped architectures have dominated medical image segmentation, which is crucial for various clinical purposes. However, the inherent locality of convolution makes CNNs fail to fully exploit global context,
Externí odkaz:
http://arxiv.org/abs/2202.12295
The canonical polyadic decomposition (CPD) is a fundamental tensor decomposition which expresses a tensor as a sum of rank one tensors. In stark contrast to the matrix case, with light assumptions, the CPD of a low rank tensor is (essentially) unique
Externí odkaz:
http://arxiv.org/abs/2202.11414
The canonical polyadic decomposition (CPD) is a compact decomposition which expresses a tensor as a sum of its rank-1 components. A common step in the computation of a CPD is computing a generalized eigenvalue decomposition (GEVD) of the tensor. A GE
Externí odkaz:
http://arxiv.org/abs/2112.08303
Autor:
Evert, Eric, De Lathauwer, Lieven
The canonical polyadic decomposition (CPD) of a low rank tensor plays a major role in data analysis and signal processing by allowing for unique recovery of underlying factors. However, it is well known that the low rank CPD approximation problem is
Externí odkaz:
http://arxiv.org/abs/2112.08283
The proposed article aims at offering a comprehensive tutorial for the computational aspects of structured matrix and tensor factorization. Unlike existing tutorials that mainly focus on {\it algorithmic procedures} for a small set of problems, e.g.,
Externí odkaz:
http://arxiv.org/abs/2006.08183
Autor:
Chatzichristos, Christos, Kofidis, Eleftherios, De Lathauwer, Lieven, Theodoridis, Sergios, Van Huffel, Sabine
Data fusion refers to the joint analysis of multiple datasets which provide complementary views of the same task. In this preprint, the problem of jointly analyzing electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) data is
Externí odkaz:
http://arxiv.org/abs/2005.07134
Publikováno v:
28th European Signal Processing Conference (EUSIPCO), 2021, pp. 1020-1024
The decomposition of tensors into simple rank-1 terms is key in a variety of applications in signal processing, data analysis and machine learning. While this canonical polyadic decomposition (CPD) is unique under mild conditions, including prior kno
Externí odkaz:
http://arxiv.org/abs/2003.03502
Autor:
Domanov, Ignat, De Lathauwer, Lieven
Decompositions of higher-order tensors into sums of simple terms are ubiquitous. We show that in order to verify that two tensors are generated by the same (possibly scaled) terms it is not necessary to compute the individual decompositions. In gener
Externí odkaz:
http://arxiv.org/abs/1912.04694
Autor:
Berger, Guillaume O., Absil, P. -A., De Lathauwer, Lieven, Jungers, Raphaël M., Van Barel, Marc
Invariance transformations of polyadic decompositions of matrix multiplication tensors define an equivalence relation on the set of such decompositions. In this paper, we present an algorithm to efficiently decide whether two polyadic decompositions
Externí odkaz:
http://arxiv.org/abs/1902.03950