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2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011
Noise is an important concern in high-angular resolution diffusion imaging studies because it can lead to errors in downstream analyses of white matter structure. To address this issue, we investigate a new approach for denoising diffusion-weighted data sets based on the K-SVD algorithm.
Vishal Patel +3 more
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Noise is an important concern in high-angular resolution diffusion imaging studies because it can lead to errors in downstream analyses of white matter structure. To address this issue, we investigate a new approach for denoising diffusion-weighted data sets based on the K-SVD algorithm.
Vishal Patel +3 more
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Bisection and twisted SVD on GPU
2015 IEEE High Performance Extreme Computing Conference (HPEC), 2015Singular value decomposition (SVD) is one of the most important factorizations in matrix computation. However, computing SVD is still time-consuming, especially when the dimension of matrices exceeds tens of thousands. In this paper, we present a high performance approach called “Bisection and Twisted” (BT) for solving bidiagonal SVD. As modern general
Lu He +6 more
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Stability and Performance of the SVD System
IEEE Transactions on Automatic Control, 2017This paper presents theoretical guarantees for the stability and performance for the singular value decomposition (SVD) system. The SVD System uses the SVD to reduce the dimension of the control inputs within a feedback loop. This is used to meet the rank 1 input constraint imposed by the row-column structure.
Ryder C. Winck, Wayne J. Book
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On the applications of SVD in fault diagnosis
SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483), 2004Practically measured time series signals are reconstructed in phase space. By using singular value decomposition (SVD) theory, the method of singular spectrum characteristic based on the reconstructed attractor track matrix is proposed to increase the signal noise ratio (SNR), then the detection of abrupt information and early rub-impact detection of ...
Ruixin Li +3 more
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Neurology, 2014
In their article “Stroke subtype, vascular risk factors, and total MRI brain small-vessel disease burden,” Dr. Staals and colleagues1 at the University of Edinburgh (Scotland) looked at a new way of assessing strokes. Their goal was to see whether they could design a simpler way to use MRI in the evaluation of certain kinds of stroke.
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In their article “Stroke subtype, vascular risk factors, and total MRI brain small-vessel disease burden,” Dr. Staals and colleagues1 at the University of Edinburgh (Scotland) looked at a new way of assessing strokes. Their goal was to see whether they could design a simpler way to use MRI in the evaluation of certain kinds of stroke.
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Acceleration of SVD routines in LAPACK
Eurocon 2013, 2013Singular Value Decomposition is widely used in many different engineering applications like Image Processing, Image Compression, Principle Component Analysis etc. It is also very useful in statistical analysis for Least Squares Approximations, and Data Clustering for reducing data dimensionality.
Chaitanya Gunta +3 more
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W-Cycle SVD: A Multilevel Algorithm for Batched SVD on GPUs
SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, 2022Junmin Xiao +8 more
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PCA and SVD with nonnegative loadings
Pattern Recognition, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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2013
The article considers an approach to solving a Fredholm integral equation of the first kind by truncated SVD decomposition using the generalized residual principle for determining the optimal solution.
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The article considers an approach to solving a Fredholm integral equation of the first kind by truncated SVD decomposition using the generalized residual principle for determining the optimal solution.
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