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Visual Studio Magazine1y
Principal Component Analysis from Scratch Using Singular Value Decomposition with C#
Understanding Singular Value Decomposition If you have a matrix A with dim = n, it is possible to compute n eigenvalues (ordinary numbers like 1.234) and n associated eigenvectors, each with n values.
IEEE2y
A Reliable Singular Value Decomposition and Geometric Mean Decomposition Based Precoding Scheme for MU-MIMO-VLC System - IEEE Xplore
In this paper, in order to improve the reliability of Multi-User Multiple-Input Multiple-Output Visible Light Communication (MU-MIMO-VLC) system, we propose a precoding scheme that based on Singular ...
Frontiers3y
Improvements of the Sub-Seasonal Precipitation Predicted by the BCC_S2S Forecast System Over Eastern China in Summer Using the Singular Value Decomposition Bias ... - Frontiers
A bias correction scheme has been developed based on the singular value decomposition (SVD) analysis in this study, ... The 20-year mean TCC values produced by BCC_S2SFS with 0–10 days, 11–20 days, ...
IEEE3y
Tensor Product and Tensor-Singular Value Decomposition Based Multi-Exposure Fusion of Images - IEEE Xplore
For the luminance fusion, the luminance channel of multi-exposure images is divided into two parts, that is, de-mean term and mean term. The de-mean term is represented as a tensor to extract the ...
Oak Ridge National Lab4y
A Survey of Singular Value Decomposition Methods for Distributed Tall/Skinny Data
The Singular Value Decomposition (SVD) is one of the most important matrix factorizations, enjoying a wide variety of applications across numerous application domains. In statistics and data analysis, ...