Results 261 to 270 of about 9,171,861 (296)

Unfolded RPCA Network for Mitigating Inter-Transmitter Code Interference in MIMO PMCW Systems. [PDF]

Sensors (Basel)
Lee Y, Lee JH, Lee S.
europepmc   +1 more source

Structured low-rank approximation and its applications

Automatica, 2008
Fitting data by a bounded complexity linear model is equivalent to low-rank approximation of a matrix constructed from the data. The data matrix being Hankel structured is equivalent to the existence of a linear time-invariant system that fits the data ...
Ivan Markovsky
exaly   +2 more sources

Dynamical Low‐Rank Approximation [PDF]

SIAM Journal on Matrix Analysis and Applications, 2007
For the low-rank approximation of time-dependent data matrices and of solutions to matrix differential equations, an increment-based computational approach is proposed and analyzed. In this method, the derivative is projected onto the tangent space of the manifold of rank-$r$ matrices at the current approximation.
Othmar Koch, Christian Lubich
exaly   +2 more sources

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Convex Low Rank Approximation

International Journal of Computer Vision, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Viktor Larsson, Carl Olsson
openaire   +2 more sources

Low Rank Approximation

2012
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequently in many different fields. Low Rank Approximation: Algorithms, Implementation, Applications is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation.
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Low-rank approximations for dynamic imaging

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011
This paper describes a framework for dynamic imaging based on the representation of a spatiotemporal image as a low-rank matrix. This kind of image modeling is flexible enough to accurately and parsimoniously represent a wide range of dynamic imaging data.
Justin P. Haldar, Zhi-Pei Liang
openaire   +1 more source

Adaptive Low Rank Approximation for Tensors

2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015
In this paper, we propose a novel framework for finding low rank approximation of a given tensor. This framework is based on the adaptive lasso with coefficient weights for sparse computation in tensor rank detection. We also provide an algorithm for solving the adaptive lasso model problem for tensor approximation.
Xiaofei Wang, Carmeliza Navasca
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Nonlinearly Structured Low-Rank Approximation

2014
Polynomially structured low-rank approximation problems occur in algebraic curve fitting, e.g., conic section fitting, subspace clustering (generalized principal component analysis), and nonlinear and parameter-varying system identification.
Markovsky, Ivan, Usevich, Konstantin
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Structured weighted low rank approximation

Numerical Linear Algebra with Applications, 2004
AbstractThis paper extends the weighted low rank approximation (WLRA) approach towards linearly structured matrices. In the case of Hankel matrices an equivalent unconstrained optimization problem is derived and an algorithm for solving it is proposed. The correctness of the latter algorithm is verified on a benchmark problem.
M. Schuermans, Philippe Lemmerling, Sabine Van Huffel   +2 more
openaire   +1 more source

Applications of structured low-rank approximation

IFAC Proceedings Volumes, 2009
Abstract A number of problems in system theory, signal processing, and computer algebra fit into a generic structured low-rank approximation problem. Several problems of this type are reviewed and efficient local optimization methods for solving them are outlined.
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