Results 141 to 150 of about 44,503 (183)

Stacking Using Truncated Singular Value Decomposition and Local Similarity

78th EAGE Conference and Exhibition 2016, 2016
The 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, B.R Di, J.X Wei, Q. Xie, S.H Zu, Y.K Chen   +5 more
openaire   +1 more source

Approximate convolution using partitioned truncated singular value decomposition filtering

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
In 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
openaire   +1 more source

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, Tong Li, Yangbo Ye, Jianfeng Cai, Weiyu Xu, Palle E. T. Jorgensen   +5 more
openaire   +1 more source

TP Model Transformation Via Sequentially Truncated Higher‐Order Singular Value Decomposition

Asian Journal of Control, 2014
AbstractThe sequentially truncated higher‐order singular value decomposition (ST‐HOSVD) is applied to a tensor product (TP) model transformation instead of the compact form of HOSVD (CHOSVD). The goal is to reduce computational cost in the transformation.
Pan, Junjun, Lu, Linzhang
openaire   +2 more sources

Interior reconstruction using the truncated Hilbert transform via singular value decomposition

Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 2008
The state-of-the-art technology for theoretically exact local computed tomography (CT) is to reconstruct an object function using the truncated Hilbert transform (THT) via the projection onto convex sets (POCS) method, which is iterative and computationally expensive.
Hengyong, Yu, Yangbo, Ye, Ge, Wang
openaire   +2 more sources

Singular value decomposition for the truncated Hilbert transform

Inverse Problems, 2010
Starting 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.
openaire   +2 more sources

Truncated singular value decomposition method for calibrating a Stokes polarimeter

SPIE Proceedings, 2007
We 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, Jessica C. Ramella-Roman, Thomas A. Germer   +2 more
openaire   +1 more source

Accelerating Dynamic MRI Reconstruction Using Adaptive Sequentially Truncated Higher-Order Singular Value Decomposition

Current Medical Imaging Formerly Current Medical Imaging Reviews, 2022
Background: Dynamic magnetic resonance imaging (dMRI) plays an important role in cardiac perfusion and functional clinical exams. However, further applications are limited by the speed of data acquisition. Objective: A low-rank plus sparse decomposition approach is often introduced for reconstructing dynamic magnetic resonance imaging (dMRI) from ...
Yang Li, Qiannan Shen, Mingfeng Jiang, Lingyan Zhu, Yongming Li, Pin Wang, Tie-Qiang Li   +6 more
openaire   +2 more sources

An improved extreme learning algorithm based on truncated singular value decomposition

The 27th Chinese Control and Decision Conference (2015 CCDC), 2015
With respect to the ill-posed problem when calculating output weights of the ELM (Extreme Learning Machine), an improved ELM algorithm based on TSVD (Truncated Singular Value Decomposition) is proposed in this paper. The degree of ill-condition is severe if the hidden layer output matrix has a large condition number.
Jianhui Wang, Xiao Wang, Shusheng Gu, Wang Liao, Xiaoke Fang   +4 more
openaire   +1 more source

A modified truncated singular value decomposition method for discrete ill-posed problems.

Numerical Linear Algebra with Applications, 2014
Summary: 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 \(A_k\).
NOSCHESE, Silvia, Lothar Reichel
openaire   +2 more sources