Searched for: subject%3A%22Low%255C-rank%255C+approximation%22
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Ban, Hanyuan (author)
Gaussian process regression (GPR), a potent non-parametric data modeling tool, has gained attention but is hindered by its high com- putational load. State-of-the-art low-rank approximations like struc- tured kernel interpolation (SKI)-based methods offer efficiency, yet lack a strategy for determining the number of grid points, a pivotal factor...
master thesis 2023
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Menzen, C.M. (author), Kok, M. (author), Batselier, K. (author)
Multiway data often naturally occurs in a tensorial format which can be approximately represented by a low-rank tensor decomposition. This is useful because complexity can be significantly reduced and the treatment of large-scale data sets can be facilitated. In this paper, we find a low-rank representation for a given tensor by solving a...
journal article 2022
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Kuś, G.I. (author), van der Zwaag, S. (author), Bessa, M.A. (author)
Gaussian processes are well-established Bayesian machine learning algorithms with significant merits, despite a strong limitation: lack of scalability. Clever solutions address this issue by inducing sparsity through low-rank approximations, often based on the Nystrom method. Here, we propose a different method to achieve better scalability...
journal article 2021
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Zhang, Jie (author), Chen, Huawei (author), Hendriks, R.C. (author)
Multi-microphone speech enhancement methods typically require a reference position with respect to which the target signal is estimated. Often, this reference position is arbitrarily chosen as one of the reference microphones. However, it has been shown that the choice of the reference microphone can have a significant impact on the final...
journal article 2020
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Wu, Juan (author), Bai, Min (author), Zhang, D. (author), Wang, Hang (author), Huang, Guangtan (author), Chen, Yangkang (author)
Five-dimensional (5D) seismic data reconstruction becomes more appealing in recent years because it takes advantage of five physical dimensions of the seismic data and can reconstruct data with large gap. The low-rank approximation approach is one of the most effective methods for reconstructing 5D dataset. However, the main disadvantage of...
journal article 2020
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Sinquin, B. (author), Verhaegen, M.H.G. (author)
In this paper we address the identification of (2D) spatial-temporal dynamical systems governed by the Vector Auto-Regressive (VAR) form. The coefficient-matrices of the VAR model are parametrized as sums of Kronecker products. When the number of terms in the sum is small compared to the size of the matrix, such a Kronecker representation...
conference paper 2017
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Qiu, Y. (author), Van Gijzen, M.B. (author), Van Wingerden, J.W. (author), Verhaegen, M. (author), Vuik, C. (author)
In this paper, we consider preconditioning for PDE-constrained optimization problems. The underlying problems yield a linear saddle-point system. We study a class of preconditioners based on multilevel sequentially semiseparable (MSSS) matrix computations. The novel global preconditioner is to make use of the global structure of the saddle-point...
report 2014
Searched for: subject%3A%22Low%255C-rank%255C+approximation%22
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