Difference between revisions of "Multidimensional Scaling"

From Cohen Courses
Jump to navigationJump to search
(Created page with 'Multidimensional scaling (MDS) takes input a <math> n \times n <math> matrix of distances <math> D </math> where <math> D_{ij} </math> denotes the target distance between entity…')
 
Line 1: Line 1:
Multidimensional scaling (MDS) takes input a <math> n \times n <math> matrix of distances <math> D </math> where <math> D_{ij} </math>  denotes the target distance between entity <math> i </math> and entity <math> j </math>. It produces an <math> n \times p </math> matrix <math> X </math> where the <math> i</math>th row is the position in ''p''-dimensional latent space. MDS transforms the pairwise distance matrix D into a similarity matrix \tilde D using linear transformations
+
Multidimensional scaling (MDS) takes input a <math> n \times n </math> matrix of distances <math> D </math> where <math> D_{ij} </math>  denotes the target distance between entity <math> i </math> and entity <math> j </math>. It produces an <math> n \times p </math> matrix <math> X </math> where the <math> i</math>th row is the position in ''p''-dimensional latent space. MDS transforms the pairwise distance matrix D into a similarity matrix \tilde D using linear transformations
  
 
[http://en.wikipedia.org/wiki/Multidimensional_scaling External Link]
 
[http://en.wikipedia.org/wiki/Multidimensional_scaling External Link]

Revision as of 17:55, 1 April 2011

Multidimensional scaling (MDS) takes input a matrix of distances where denotes the target distance between entity and entity . It produces an matrix where the th row is the position in p-dimensional latent space. MDS transforms the pairwise distance matrix D into a similarity matrix \tilde D using linear transformations

External Link

Relevant Papers