Nonnegative singular value decomposition for microarray data analysis of spermatogenesis

2008 ◽  
Author(s):  
Weixiang Liu ◽  
Aifa Tang ◽  
Datian Ye ◽  
Zhen Ji
Keyword(s):  
Data Analysis ◽  
Singular Value Decomposition ◽  
Microarray Data ◽  
Singular Value ◽  
Microarray Data Analysis ◽  
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 Analysis and modelling of septic shock microarray data using Singular Value Decomposition

2017 ◽  
Vol 70 ◽  
pp. 77-84
Author(s):  
Srinivas Allanki ◽  
Madhulika Dixit ◽  
Paul Thangaraj ◽  
Nandan Kumar Sinha
Keyword(s):  
Septic Shock ◽  
Singular Value Decomposition ◽  
Microarray Data ◽  
Singular Value ◽  
Value Decomposition
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