de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Graph Based Semi-Supervised Learning with Sharper Edges

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84217

Shin,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83968

Hill,  NJ
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84153

Rätsch,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Shin, H., Hill, N., & Rätsch, G. (2006). Graph Based Semi-Supervised Learning with Sharper Edges. Machine Learning: ECML 2006, 401-412.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D047-A
Abstract
In many graph-based semi-supervised learning algorithms, edge weights are assumed to be fixed and determined by the data pointsamp;amp;amp;amp;lsquo; (often symmetric)relationships in input space, without considering directionality. However, relationships may be more informative in one direction (e.g. from labelled to unlabelled) than in the reverse direction, and some relationships (e.g. strong weights between oppositely labelled points) are unhelpful in either direction. Undesirable edges may reduce the amount of influence an informative point can propagate to its neighbours -- the point and its outgoing edges have been ``blunted.amp;amp;amp;amp;lsquo;amp;amp;amp;amp;lsquo; We present an approach to ``sharpeningamp;amp;amp;amp;lsquo;amp;amp;amp;amp;lsquo; in which weights are adjusted to meet an optimization criterion wherever they are directed towards labelled points. This principle can be applied to a wide variety of algorithms. In the current paper, we present one ad hoc solution satisfying the principle, in order to show that it can improve performance on a number of publicly available benchmark data sets.