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PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84206

Seldin,  Y
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Seldin, Y. (2009). PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering. In the proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS 2009), 472-479.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C54B-8
Zusammenfassung
We derive a PAC-Bayesian generalization bound for density estimation. Similar to the PAC-Bayesian generalization bound for classification, the result has the appealingly simple form of a tradeoff between empirical performance and the KL-divergence of the posterior from the prior. Moreover, the PAC-Bayesian generalization bound for classification can be derived as a special case of the bound for density estimation. To illustrate a possible application of our bound we derive a generalization bound for co-clustering. The bound provides a criterion to evaluate the ability of co-clustering to predict new co-occurrences, thus introducing a supervised flavor to this traditionally unsupervised task.