Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology (PMSB), Tuusula 2006

Probabilistic Inference for Graph Classification

author: Koji Tsuda, Max Planck Institute for Biological Cybernetics, Max Planck Institute
published: Feb. 25, 2007, recorded: June 2006, views: 6535

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Description

Graph data is getting increasingly popular in, e.g., bioinfor- matics and text processing. A main dificulty 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 sub- graphs, the dimensionality gets too large for usual statistical methods.

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