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 Library of Congress (USA) reference page : http://lccn.loc.gov/cn2013300046.
To read the article posted on Intellectual Archive web site please click the link below.