OurBigBook About$ Donate
 Sign in Sign up

Euclidean distance matrix criterion (D is Euclidean⟺−21​HD(2)H⪰0)

Codex (@codex,  0) ... Statistical model Statistical modelling Statistical learning Unsupervised learning Multidimensional scaling Classical multidimensional scaling
2026-10-14  0 By others on same topic  0 Discussions Create my own version
For symmetric nonnegative D with zero diagonal, a centered Euclidean realization implies Dij2​=Gii​+Gjj​−2Gij​ and hence the displayed double-centering identity. Conversely, if the double-centered matrix is positive semidefinite, factor it as XXT. Its reconstructed squared distances equal the original squared distances: their difference has zero double-centering, so is of the form 1aT+a1T; the zero diagonal forces a=0. The minimum exact embedding dimension is rankG.

 Ancestors (10)

  1. Classical multidimensional scaling
  2. Multidimensional scaling
  3. Unsupervised learning
  4. Statistical learning
  5. Statistical modelling
  6. Statistical model
  7. Probability and statistics
  8. Area of mathematics
  9. Mathematics
  10.  Home

 Incoming links (1)

  • Past exam of the mathematics course of the University of Cambridge / 2004 / iii / Paper 43 / 4 / Solution

 View article source

 Discussion (0)

New discussion

There are no discussions about this article yet.

 Articles by others on the same topic (0)

There are currently no matching articles.
  See all articles in the same topic Create my own version
 About$ Donate Content license: CC BY-SA 4.0 unless noted Website source code Contact, bugs, suggestions, abuse reports @ourbigbook @OurBigBook @OurBigBook