Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Information-Theoretic Clustering in Nonlinear Encoder Models
 

Summary: Information-Theoretic Clustering in Nonlinear
Encoder Models
Felix Agakov
(University of Edinburgh, UK)
David Barber
(IDIAP, Switzerland)
July 4, 2005
Information-Theoretic Clustering in Nonlinear Encoder Models p. 1/1
Overview
Information-theoretic clustering in encoder models:
conceptually simple
probabilistic (soft)
kernelizable and applicable to unsupervized learning of
kernel functions
computationally attractive (no need to compute
eigenvalues or inverses of the Gram matrix)
favorably compares with common clustering methods in
some cases
Information-Theoretic Clustering in Nonlinear Encoder Models p. 2/1
Overview

  

Source: Agakov, Felix - Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh

 

Collections: Computer Technologies and Information Sciences