Advertisement

Low Rank Approximations

  • David Forsyth
Chapter

Abstract

A principal components analysis models high dimensional data points with an accurate, low dimensional, model. Now form a data matrix from the approximate points. This data matrix must have low rank (because the model is low dimensional) and it must be close to the original data matrix (because the model is accurate). This suggests modelling data with a low rank matrix.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • David Forsyth
    • 1
  1. 1.Computer Science DepartmentUniversity of Illinois Urbana ChampaignUrbanaUSA

Personalised recommendations