Independent Subspace Analysis Using k-Nearest Neighborhood Distances

  • Barnabás Póczos
  • András Lőrincz
Conference paper

DOI: 10.1007/11550907_27

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3697)
Cite this paper as:
Póczos B., Lőrincz A. (2005) Independent Subspace Analysis Using k-Nearest Neighborhood Distances. In: Duch W., Kacprzyk J., Oja E., Zadrożny S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg

Abstract

A novel algorithm called independent subspace analysis (ISA) is introduced to estimate independent subspaces. The algorithm solves the ISA problem by estimating multi-dimensional differential entropies. Two variants are examined, both of them utilize distances between the k-nearest neighbors of the sample points. Numerical simulations demonstrate the usefulness of the algorithms.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Barnabás Póczos
    • 1
  • András Lőrincz
    • 1
  1. 1.Department of Information Systems, Eötvös Loránd University, Research Group on Intelligent Information SystemsHungarian Academy of SciencesBudapestHungary

Personalised recommendations