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
core +6 more sources
Seismic Random Noise Attenuation in the Laplace Domain Using Singular Value Decomposition
IEEE Access, 2021 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
doaj +1 more source
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
openaire +2 more sources
Singular value decomposition applied to compact binary coalescence gravitational-wave signals [PDF]
, 2010 We investigate the application of the singular value decomposition to compact-binary, gravitational-wave data-analysis. We find that the truncated singular value decomposition reduces the number of filters required to analyze a given region of parameter ...
Adrian Chapman +7 more
core +2 more sources
Robust Parameter Estimation of an Empirical Manoeuvring Model Using Free-Running Model Tests
Journal of Marine Science and Engineering, 2021 The work presents the identification and validation of the hydrodynamic coefficients for the surge, sway, and yaw motion. This is performed in two ways: using simulated data and free-running test data.
Ana Catarina Costa +2 more
doaj +1 more source
Early stopping for statistical inverse problems via truncated SVD estimation [PDF]
, 2018 We consider truncated SVD (or spectral cut-off, projection) estimators for a prototypical statistical inverse problem in dimension $D$. Since calculating the singular value decomposition (SVD) only for the largest singular values is much less costly than
Blanchard, Gilles +2 more
core +4 more sources
IEEE Photonics Journal, 2023 Currently, the engineering of miniature spectrometers mainly faces three problems: the mismatch between the number of filters at the front end of the detector and the spectral reconstruction accuracy; the lack of a stable spectral reconstruction ...
Jiakun Zhang +3 more
doaj +1 more source
OptShrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage [PDF]
, 2014 The truncated singular value decomposition (SVD) of the measurement matrix is the optimal solution to the_representation_ problem of how to best approximate a noisy measurement matrix using a low-rank matrix.
Nadakuditi, Raj Rao
core +1 more source
New Parametric Imaging Method with Fluorescein Angiograms for Detecting Areas of Capillary Nonperfusion [PDF]
Healthcare Informatics Research, 2014 ObjectivesFluorescein angiography (FAG) is currently the most useful diagnostic modality for examining retinal circulation, and it is frequently used for the evaluation of patients with diabetic retinopathy, occlusive diseases, such as retinal venous and
Young Jae Kim +5 more
doaj +1 more source
Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization [PDF]
, 2018 We present a reconstruction method involving maximum-likelihood expectation maximization (MLEM) to model Poisson noise as applied to fluorescence molecular tomography (FMT). MLEM is initialized with the output from a sparse reconstruction-based approach,
Jha, Abhinav K +3 more
core +3 more sources