English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Measuring Statistical Dependence with Hilbert-Schmidt Norms

Gretton, A., Bousquet, O., Smola, A., & Schölkopf, B.(2005). Measuring Statistical Dependence with Hilbert-Schmidt Norms (140). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D569-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-881C-6
Genre: Report

Files

show Files
hide Files
:
MPIK-TR-140.pdf (Publisher version), 252KB
Name:
MPIK-TR-140.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Gretton, A1, 2, Author              
Bousquet, O, Author              
Smola, AJ, Author
Schölkopf, B1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

Details

show
hide
Language(s):
 Dates: 2005-06
 Publication Status: Published in print
 Pages: 16
 Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics
 Table of Contents: -
 Rev. Method: -
 Identifiers: Report Nr.: 140
BibTex Citekey: 3437
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Technical Report of the Max Planck Institute for Biological Cybernetics
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 140 Sequence Number: - Start / End Page: - Identifier: -