Covariance matrix filtering with bootstrapped hierarchies

HIGHLIGHTS

• who: Christian Bongiorno and Damien Challet from the Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, Gif-sur-Yvette, France Editor:, IT University of have published the article: Covariance matrix filtering with bootstrapped hierarchies, in the Journal: PLOS ONE of August/27,/2020
• what: The authors propose here a probabilistic hierarchical clustering method named Bootstrapped Average Hierarchical Clustering (BAHC) that is particularly effective in the high-dimensional case i.e. when there are more objects than features. The authors compare the out-of-sample risk computed from . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.