Entire regularization paths for graph data

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.

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Koji Tsuda
• Keywords: Frequent Substructure Mining, Graph Data Processing, ICML 2007, Machine Learning, Regularization Path Tracking