Zobrazeno 1 - 10
of 486
pro vyhledávání: '"Comon, Pierre"'
Hyperspectral unmixing allows representing mixed pixels as a set of pure materials weighted by their abundances. Spectral features alone are often insufficient, so it is common to rely on other features of the scene. Matrix models become insufficient
Externí odkaz:
http://arxiv.org/abs/2310.03860
Publikováno v:
European Signal Processing Conference, Aug 2022, Belgrade, Serbia
Performances of the Multivariate Kurtosis are investigated when applied to colored data, with or without Auto-Regressive pre-whitening, and with or without projection onto a lower-dimensional random subspace. Computer experiments demonstrate the impo
Externí odkaz:
http://arxiv.org/abs/2206.06828
Publikováno v:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2022, Singapore, Singapore
Extensive literature exists on how to test for normality, especially for identically and independently distributed (i.i.d) processes. The case of dependent samples has also been addressed, but only for scalar random processes. For this reason, we hav
Externí odkaz:
http://arxiv.org/abs/2110.03927
Most normality tests in the literature are performed for scalar and independent samples. Thus, they become unreliable when applied to colored processes, hampering their use in realistic scenarios.We focus on Mardia's multivariate kurtosis, derive clo
Externí odkaz:
http://arxiv.org/abs/2109.08427
Tensor models play an increasingly prominent role in many fields, notably in machine learning. In several applications, such as community detection, topic modeling and Gaussian mixture learning, one must estimate a low-rank signal from a noisy tensor
Externí odkaz:
http://arxiv.org/abs/2108.00774
In this paper, we propose a gradient-based block coordinate descent (BCD-G) framework to solve the joint approximate diagonalization of matrices defined on the product of the complex Stiefel manifold and the special linear group. Instead of the cycli
Externí odkaz:
http://arxiv.org/abs/2009.13377
Publikováno v:
IEEE SAM 2020
Jacobi-type algorithms for simultaneous approximate diagonalization of real (or complex) symmetric tensors have been widely used in independent component analysis (ICA) because of their good performance. One natural way of choosing the index pairs in
Externí odkaz:
http://arxiv.org/abs/1912.07194
In this paper, we propose a Jacobi-type algorithm to solve the low rank orthogonal approximation problem of symmetric tensors. This algorithm includes as a special case the well-known Jacobi CoM2 algorithm for the approximate orthogonal diagonalizati
Externí odkaz:
http://arxiv.org/abs/1911.00659
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