Information theoretic learning and kernel methods

  • Jenssen R
  • 3


    Mendeley users who have this article in their library.
  • 10


    Citations of this article.


In this chapter, we discuss important connections between two different approaches to machine learning, namely Renyi entropy-based information theoretic learning and the Mercer kernel methods. We show that Parzen windowing for estimation of probability density functions reveals the connections, enabling the information theoretic criteria to be expressed in terms of mean vectors in a Mercer kernel feature space, or equivalently, in terms of kernel matrices. From this we learn not only that two until now separate paradigms in machine learning are related, it also enables us to interpret and understand methods developed in one paradigm in terms of the other, and to develop new sophisticated machine learning algorithms based on both approaches.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Robert Jenssen

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free