multidimensional scaling


Multidimensional scaling is a method for visualizing similarity between data points by embedding the data into a low-dimensional subspace. The locations are chosen so that the distances in the embedding space match the dissimilarities as closely as possible.


This concept has the prerequisites:


  • Know what stress function multidimensional scaling is minimizing
  • Understand how it can be used to visualize similarity data
  • Be aware that the exact solution can be computed efficiently

Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)

Supplemental resources (the following are optional, but you may find them useful)


The Elements of Statistical Learning
A graudate-level statistical learning textbook with a focus on frequentist methods.
Authors: Trevor Hastie,Robert Tibshirani,Jerome Friedman


See also