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Singular Value Decomposition (SVD) is the process of decomposing a matrix A this way:
 
Singular Value Decomposition (SVD) is the process of decomposing a matrix A this way:
  
<math>\Biggl\[ A \Biggr]</math>
+
<math>\left [ \bold{A} \right ] = \left [ \bold{U} \right ] \cdot \left [ \bold{\Sigma} \right ] \cdot \left [ \bold{V} \right ]^T</math>
 +
 
 +
[[File:SVD.png]]
  
= \Biggl\[ U \Biggr] \dot \Biggl\[ \Sigma \Biggr] \dot \Biggl\[ V \Biggr]^T</math>
 
  
 
=== LU Decomposition ===
 
=== LU Decomposition ===

Revision as of 16:34, 30 July 2017

Matrix Decomposition Methods

QR Decomposition

SVD Decomposition

Singular Value Decomposition (SVD) is the process of decomposing a matrix A this way:

<math>\left [ \bold{A} \right ] = \left [ \bold{U} \right ] \cdot \left [ \bold{\Sigma} \right ] \cdot \left [ \bold{V} \right ]^T</math>

SVD.png


LU Decomposition