English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Entire Regularization Paths for Graph Data

Tsuda, K. (2007). Entire Regularization Paths for Graph Data. Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007), 919-926.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Tsuda, K1, Author           
Ghahramani, Z., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

Content

show
hide
Free keywords: -
 Abstract: Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high dimensionality of graphs, namely, when a graph is represented as a binary feature vector of indicators of all possible subgraph patterns, the dimensionality gets too large for usual statistical methods. We propose an efficient method to select a small number of salient patterns by regularization path tracking. The generation of useless patterns is minimized by progressive extension of the search space. In experiments, it is shown that our technique is considerably more efficient than a simpler approach based on frequent substructure mining.

Details

show
hide
Language(s):
 Dates: 2007-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: URI: http://oregonstate.edu/conferences/icml2007/
DOI: 10.1145/1273496.1273612
BibTex Citekey: 4451
 Degree: -

Event

show
hide
Title: 24th Annual International Conference on Machine Learning
Place of Event: Corvallis, OR, USA
Start-/End Date: -

Legal Case

show

Project information

show

Source 1

show
hide
Title: Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007)
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 919 - 926 Identifier: -