A New Truncation Strategy for the Higher-Order Singular Value Decomposition [PDF]
SIAM Journal on Scientific Computing, 2012 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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Observation and Inversion of Aerosol Particle Size Distribution over Yinchuan Area
Atmosphere, 2021 Particle size distribution is one of the important microphysical parameters to characterize the aerosol properties. The aerosol optical depth is used as the function of wavelength to study the particle size distribution of whole atmospheric column ...
Jiandong Mao +4 more
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Fractional Norm Regularization Using Truncated Singular Value Decomposition
SSRN Electronic Journal, 2022 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
Axioms, 2021 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]
Communications in Statistics - Simulation and Computation, 2015 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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Vector extrapolation applied to truncated singular value decomposition and truncated iteration [PDF]
Journal of Engineering Mathematics, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bouhamidi, A. +4 more
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DEBLURRING STUDY OF DMSP/OLS NIGHTTIME LIGHT DATA BY RTSVD [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 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
Remote Sensing, 2023 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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Singular Value Statistics of Matrix Products with Truncated Unitary Matrices [PDF]
International Mathematics Research Notices, 2015 29 ...
Kieburg, Mario +2 more
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Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions [PDF]
, 2010 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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