Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Don't be afraid of simpler patterns
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Partial least squares regression for graph mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
L2 norm regularized feature kernel regression for graph data
Proceedings of the 18th ACM conference on Information and knowledge management
Boosting with structure information in the functional space: an application to graph classification
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
International Journal of Knowledge Discovery in Bioinformatics
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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.