PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Entire Regularization Paths for Graph Data
Koji Tsuda
In: 24th International Conference on Machine Learning, 20-24 June 2007, Corvallis, Oregon.


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:3581
Deposited By:Koji Tsuda
Deposited On:13 February 2008