Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Otto Debals"'
Publikováno v:
SIAM Journal on Scientific Computing. 41:A789-A815
© 2019 Society for Industrial and Applied Mathematics Decomposing tensors into simple terms is often an essential step toward discovering and understanding underlying processes or toward compressing data. However, storing the tensor and computing it
Publikováno v:
IEEE Transactions on Signal Processing. 65:5770-5784
Many real-life signals can be described in terms of much fewer parameters than the actual number of samples. Such compressible signals can often be represented very compactly with low-rank matrix and tensor models. The authors have adopted this strat
Publikováno v:
IFAC-PapersOnLine. 50:14150-14155
Fitting a signal to a sum-of-exponentials model is a basic problem in signal processing. It can be posed and solved as a Hankel structured low-rank matrix approximation problem. Subsequently, local optimization, subspace, and convex relaxation method
© 1991-2012 IEEE. Blind system identification (BSI) is an important problem in signal processing, arising in applications such as wireless telecommunications, biomedical signal processing, and seismic signal processing. In the past decades, tensors
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ed0cf3a5aea5be3a1e6fe0c49ebe003
https://lirias.kuleuven.be/handle/123456789/628943
https://lirias.kuleuven.be/handle/123456789/628943
© 1991-2012 IEEE. Multiway datasets are widespread in signal processing and play an important role in blind signal separation, array processing, and biomedical signal processing, among others. One key strength of tensors is that their decompositions
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf3292ea81c47f4fc2c206745d6d31c2
https://lirias.kuleuven.be/handle/123456789/609355
https://lirias.kuleuven.be/handle/123456789/609355
Publikováno v:
CAMSAP
Various parameters influence face recognition such as expression, pose, and illumination. In contrast to matrices, tensors can be used to naturally accommodate for the different modes of variation. The multilinear singular value decomposition (MLSVD)
© 2017 IEEE. Nonnegative matrix factorizationisakey toolinmany data analysis applications such as feature extraction, compression, and noise filtering. Many existing algorithms impose additional constraintstotake into account prior knowledgeandtoimp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::957fd99ac64b522fcf9aa0c6280ccf16
https://lirias.kuleuven.be/handle/123456789/586179
https://lirias.kuleuven.be/handle/123456789/586179
Autor:
Martijn Bousse, Sabine Van Huffel, Nico Vervliet, Otto Debals, Lieven De Lathauwer, Griet Goovaerts
Publikováno v:
EMBC
Cardiac arrhythmia or irregular heartbeats are an important feature to assess the risk on sudden cardiac death and other cardiac disorders. Automatic classification of irregular heartbeats is therefore an important part of ECG analysis. We propose a
© 1991-2012 IEEE. Many real-life signals are compressible, meaning that they depend on much fewer parameters than their sample size. In this paper, we use low-rank matrix or tensor representations for signal compression. We propose a new determinist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8bbe61d2d090251d58254845b662808
https://lirias.kuleuven.be/handle/123456789/554595
https://lirias.kuleuven.be/handle/123456789/554595
Publikováno v:
ACSSC
We give an overview of recent developments in numerical optimization-based computation of tensor decompositions that have led to the release of Tensorlab 3.0 in March 2016 (www.tensorlab.net). By careful exploitation of tensor product structure in me