Yevgeny Seldin, Naftali Tishby. Pac-bayesian Analysis of Co-clustering and Beyond
Submitted on: Aug 24, 2012, 19:11:55
Natural Sciences / Computer Science / Analysis of algorithms
Description: The analysis of co-clustering is extended to tree-shaped graphical models, which can be used to analyze high dimensional tensors. According to the bounds, the generalization abilities of tree-shaped graphical models depend on a trade-off between their empirical data fit and the mutual information that is propagated up the tree levels. We also formulate weighted graph clustering as a prediction problem: given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. The analysis of co-clustering easily extends to this problem and suggests that graph clustering should optimize the trade-off between empirical data fit and the mutual information that clusters preserve on graph nodes.
The abstract of this article will be published in the August 2012 issue of "Intellectual Archive Bulletin", ISSN 1929-1329.
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