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A modified truncated singular value decomposition method for discrete ill-posed problems
Numerical Linear Algebra With Applications, 2014Summary: Truncated singular value decomposition is a popular method for solving linear discrete ill-posed problems with a small to moderately sized matrix \(A\). Regularization is achieved by replacing the matrix \(A\) by its best rank-\(k\) approximant, which we denote by \(A_k\).
Silvia Noschese, Lothar Reichel
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IEEE Transactions on Cybernetics, 2019
The problem of recovering missing data of an incomplete tensor has drawn more and more attentions in the fields of pattern recognition, machine learning, data mining, computer vision, and signal processing. Researches on this problem usually share a common assumption that the original tensor is of low-rank.
Zisen Fang, Xiaolan Liu
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The problem of recovering missing data of an incomplete tensor has drawn more and more attentions in the fields of pattern recognition, machine learning, data mining, computer vision, and signal processing. Researches on this problem usually share a common assumption that the original tensor is of low-rank.
Zisen Fang, Xiaolan Liu
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Accelerating truncated singular-value decomposition
2018<p>Principal component analysis (PCA) is one of the most popular statistical procedures for dimension reduction. A modification of PCA, called robust principal component analysis (RPCA), has been studied to overcome the well-known shortness of PCA: sensitivity to outliers and corrupted data points.
HanQin Cai +5 more
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Approximate convolution using partitioned truncated singular value decomposition filtering
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013In many signal processing applications it is necessary to perform large convolutions in real-time. For systems where an exact convolution is too complex we propose an approximation using a partitioned truncated singular value decomposition (PTSVD) filter.
Joshua Atkins, Adam Strauss, Chen Zhang
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Singular value decomposition for the truncated Hilbert transform
Inverse Problems, 2010Starting from a breakthrough result by Gelfand and Graev, inversion of the Hilbert transform became a very important tool for image reconstruction in tomography. In particular, their result is useful when the tomographic data are truncated and one deals with an interior problem.
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Truncated singular value decomposition method for calibrating a Stokes polarimeter
SPIE Proceedings, 2007We present a method for calibrating polarimeters that uses a set of well-characterized reference polarizations and makes no assumptions about the optics contained in the polarimeter other than their linearity. The method requires that a matrix be constructed that contains the data acquired for each of the reference polarization states and that this ...
Bruno Boulbry +2 more
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Stacking Using Truncated Singular Value Decomposition and Local Similarity
78th EAGE Conference and Exhibition 2016, 2016The similarity-weighted stacking takes use of the local similarity between each trace and a reference as the weight to stack the NMO-corrected prestack seismic data. The selection of reference trace plays a significant role in the final performance.
J.Y Xie +5 more
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Truncated singular value decomposition for semantic-based data retrieval
2013 Third International Conference on Communications and Information Technology (ICCIT), 2013This paper addresses the increasingly encountered challenge of knowledge indexation. In the past decade, research on numerical schemes on knowledge indexation has been quite intensive. Vector space model is only based on the information contained in term weighting and does therefore not process the semantic contained in the sequence in which the words ...
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Target Signature Extraction Using Truncated Singular Value Decomposition for Electronic Protection
2021 IEEE Radar Conference (RadarConf21), 2021Recent studies show that jamming suppression can be performed using modified matched filters that are matched to the transmit waveform - target response (TWTR). In many radar applications the target backscattering is reduced to a scalar factor, which is mainly related to the radar cross section (RCS).
Heitor Albuquerque, Ric A. Romero
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A truncated singular value decomposition method for angular super-resolution in scanning radar
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015Angular super-resolution of scanning radar is an important problem in radar system. Some deconvolution methods are used to realize the angular super-resolution in scanning radar. However, the ill-posed nature of the deconvolution problem leads to the noise amplification in the angular super-resolution image.
Yulin Huang 0001 +2 more
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