A New Truncation Strategy for the Higher-Order Singular Value Decomposition [PDF]
We present an alternative strategy for truncating the higher-order singular value decomposition (T-HOSVD). An error expression for an approximate Tucker decomposition with orthogonal factor matrices is presented, leading us to propose a novel truncation strategy for the HOSVD, which we refer to as the sequentially truncated higher-order singular value ...
Vannieuwenhoven, Nick +2 more
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DEBLURRING STUDY OF DMSP/OLS NIGHTTIME LIGHT DATA BY RTSVD [PDF]
DMSP/OLS, as the earliest Nighttime light remote sensing data, has great application value and can greatly improve the data quality by solving the blurring problem existing in the data.
C. Ren +4 more
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A New Algorithm for Ill-Posed Problem of GNSS-Based Ionospheric Tomography
Ill-posedness of GNSS-based ionospheric tomography affects the stability and the accuracy of the inversion results. Truncated singular value decomposition (TSVD) is a common algorithm of ionospheric tomography reconstruction.
Debao Wen +5 more
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Fractional Norm Regularization Using Truncated Singular Value Decomposition
In a previous work, a solution to the fractional norm regularization (FNR) was discovered in a closed form and an inverse perturbation was adopted as a tool to overcome the ill condition of a matrix whose inverse is required by the fixed-point FNR. In this work, a novel computation technique, namely truncated singular value decomposition-fractional ...
Bamrung Tausiesakul +1 more
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An Improved Tikhonov-Regularized Variable Projection Algorithm for Separable Nonlinear Least Squares
In this work, we investigate the ill-conditioned problem of a separable, nonlinear least squares model by using the variable projection method. Based on the truncated singular value decomposition method and the Tikhonov regularization method, we propose ...
Hua Guo, Guolin Liu, Luyao Wang
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Difference-based Methods for Truncating the Singular Value Decomposition [PDF]
Given a noisy time series (or signal), one may wish to remove the noise from the observed series. Assuming that the noise-free series lies in some low dimensional subspace of rank r, a common approach is to embed the noisy time series into a Hankel trajectory matrix.
Evans, Dafydd, Gillard, Jonathan William
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The traces used in side-channel analysis are essential to breaking the key of encryption and the signal quality greatly affects the correct rate of key guessing.
Yuanzhen Wang +7 more
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Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions [PDF]
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-revealing QR decomposition, play a central role in data analysis and scientific computing.
Halko, Nathan +2 more
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Vector extrapolation applied to truncated singular value decomposition and truncated iteration [PDF]
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bouhamidi, A. +4 more
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Seismic Random Noise Attenuation in the Laplace Domain Using Singular Value Decomposition
We attenuated incoherent seismic noise using singular value decomposition in the Laplace domain. Laplace-domain wavefields are sensitive to small-amplitude noise contaminating the first-arrival signals due to damping in the Laplace transform; this noise ...
Wansoo Ha, Changsoo Shin
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