scholarly journals Tongue pellet data analysis by singular‐value decomposition approac

1988 ◽  
Vol 84 (S1) ◽  
pp. S127-S127
Author(s):  
Qiuzhen Xue ◽  
Robert Nadler ◽  
James Abbs
Keyword(s):  
Data Analysis ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition

The Algebraic Operation of SVD

2011 ◽  
Author(s):  
Gidon Eshel
Keyword(s):  
Data Analysis ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Algebraic Operation ◽  
Analysis Method ◽  
Data Analysis Method ◽  
Value Decomposition ◽  
Data Matrices

Chapter 4 discussed the eigenvalue/eigenvector diagonalization of a matrix. Perhaps the biggest problem for this to be very useful in data analysis is the restriction to square matrices. It has already been emphasized time and again that data matrices, unlike dynamical operators, are rarely square. The algebraic operation of the singular value decomposition (SVD) is the answer. Note the distinction between the data analysis method widely known as SVD and the actual algebraic machinery. The former uses the latter, but is not the latter. This chapter describes the method. Following the introduction to SVD, it provides some examples and applications.

scholarly journals USE OF SINGULAR-VALUE DECOMPOSITION IN GRAVITATIONAL-WAVE DATA ANALYSIS

2013 ◽  
Vol 23 ◽  
pp. 99-105
Author(s):  
DREW KEPPEL
Keyword(s):  
Parameter Estimation ◽  
Data Analysis ◽  
Singular Value Decomposition ◽  
Gravitational Wave ◽  
Computational Cost ◽  
Singular Value ◽  
Matched Filtering ◽  
Powerful Technique ◽  
Wave Data ◽  
Value Decomposition

Singular-value decomposition is a powerful technique that has been used in the analysis of matrices in many fields. In this paper, we summarize how it has been applied to the analysis of gravitational-wave data analysis. These include producing basis waveforms for matched filtering, decreasing the computational cost of searching for many waveforms, improving parameter estimation, and providing a method of waveform interpolation.

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