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Entire Regularization Paths for Graph Data


Conference Paper


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.

Author(s): Tsuda, K.
Book Title: ICML 2007
Journal: Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007)
Pages: 919-926
Year: 2007
Month: June
Day: 0
Editors: Ghahramani, Z.
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1145/1273496.1273612
Event Name: 24th Annual International Conference on Machine Learning
Event Place: Corvallis, OR, USA

Address: New York, NY, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Entire Regularization Paths for Graph Data},
  author = {Tsuda, K.},
  journal = {Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007)},
  booktitle = {ICML 2007},
  pages = {919-926},
  editors = {Ghahramani, Z. },
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2007},
  month_numeric = {6}