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On the Compression of Low Rank Matrices
SIAM Journal on Scientific Computing, 2005The authors describe a procedure for the decomposition and compression of low-rank matrices. Such matrices arise for instance in computational physics in potential theory, in fluid dynamics, in numerical simulations of electromagnetic phenomena. The decomposition of a matrix \(A\) of rank \(k\) is constructed in the form \(A=U\circ B\circ V^*\), where \
Hongwei Cheng +3 more
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Low Rank Solution of Lyapunov Equations
SIAM Journal on Matrix Analysis and Applications, 2002The Cholesky factor-alternating direction implicit algorithm is presented to compute a low rank approximation to the solution \(X\) of the Lyapunov equation \(AX+XA^T=-BB^T\) with large matrix \(A\) and right hand side of low rank. The algorithm requires only matrix-vector products and linear solvers.
Li, Jing-Rebecca, White, Jacob
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2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
In this paper, we introduce a principled algorithmic approach for Fourier ptychographic imaging of dynamic, time-varying targets. To the best of our knowledge, this setting has not been explicitly addressed in the ptychography literature. We argue that such a setting is very natural, and that our methods provide an important first step towards helping ...
Zhengyu Chen 0003 +4 more
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In this paper, we introduce a principled algorithmic approach for Fourier ptychographic imaging of dynamic, time-varying targets. To the best of our knowledge, this setting has not been explicitly addressed in the ptychography literature. We argue that such a setting is very natural, and that our methods provide an important first step towards helping ...
Zhengyu Chen 0003 +4 more
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Low-Rank Multilinear Filtering
Digital Signal ProcessingPublished by Elsevier Digital Signal Processing. ; International audience ; Linear filtering methods are well-known and have been successfully applied to system identification and equalization problems. However, when high-dimensional systems are modeled, these methods often perform unsatisfactorily due to their slow convergence and to the high number ...
Maryam Dehghan +2 more
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2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2019
Visual object tracking is an important step for many computer vision applications. The task becomes very challenging when the target undergoes heavy occlusion, background clutters, and sudden illumination variations. Methods that incorporate sparse representation and low-rank assumptions on the target particles have achieved promising results. However,
Sajid Javed +3 more
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Visual object tracking is an important step for many computer vision applications. The task becomes very challenging when the target undergoes heavy occlusion, background clutters, and sudden illumination variations. Methods that incorporate sparse representation and low-rank assumptions on the target particles have achieved promising results. However,
Sajid Javed +3 more
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2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
In this paper, we propose a low-rank tensor deconvolution problem which seeks multiway replicative patterns and corresponding activating tensors of rank-1. An alternating least squares (ALS) algorithm has been derived for the model to sequentially update loading components and the patterns.
Anh Huy Phan 0001 +2 more
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In this paper, we propose a low-rank tensor deconvolution problem which seeks multiway replicative patterns and corresponding activating tensors of rank-1. An alternating least squares (ALS) algorithm has been derived for the model to sequentially update loading components and the patterns.
Anh Huy Phan 0001 +2 more
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Low-Rank Preserving Projections
IEEE Transactions on Cybernetics, 2016As one of the most popular dimensionality reduction techniques, locality preserving projections (LPP) has been widely used in computer vision and pattern recognition. However, in practical applications, data is always corrupted by noises. For the corrupted data, samples from the same class may not be distributed in the nearest area, thus LPP may lose ...
Yuwu Lu +5 more
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Discriminative Low-Rank Tracking
2015 IEEE International Conference on Computer Vision (ICCV), 2015Good tracking performance is in general attributed to accurate representation over previously obtained targets or reliable discrimination between the target and the surrounding background. In this work, we exploit the advantages of the both approaches to achieve a robust tracker.
Yao Sui, Yafei Tang, Li Zhang 0023
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IEEE Transactions on Signal Processing, 1996
We introduce a class of adaptive filters based on sequential adaptive eigendecomposition (subspace tracking) of the data covariance matrix. These new algorithms are completely rank revealing, and hence, they can perfectly handle the following two relevant data cases where conventional recursive least squares (RLS) methods fail to provide satisfactory ...
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We introduce a class of adaptive filters based on sequential adaptive eigendecomposition (subspace tracking) of the data covariance matrix. These new algorithms are completely rank revealing, and hence, they can perfectly handle the following two relevant data cases where conventional recursive least squares (RLS) methods fail to provide satisfactory ...
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2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019
Visual object tracking is an important step for many computer vision applications. Visual tracking becomes more challenging when the target object observes severe occlusion, lighting variations, background clutter, and deformation difficulties to name a few.
Sajid Javed +2 more
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Visual object tracking is an important step for many computer vision applications. Visual tracking becomes more challenging when the target object observes severe occlusion, lighting variations, background clutter, and deformation difficulties to name a few.
Sajid Javed +2 more
openaire +1 more source