Ammar, Sourour

Defourny, Boris

Leray, Philippe

Wehenkel, Louis

International audienceIn this work we explore the Perturb and Combine idea, celebrated in supervised learning, in the context of probability density estimation in high-dimensional spaces with graphical probabilistic models. We propose a new family of unsupervised learning methods of mixtures of large ensembles of randomly generated tree or poly-tree structures. The specific feature of these methods is their scalability to very large numbers of variables and training instances. We explore various simple variants of these methods empirically on a set of discrete test problems of growing complexity

HAL-Univ-Nantes

High-dimensional probability density estimation with randomized ensembles of tree structured bayesian networks

Open Repository and Bibliography - Liège

https://hal.archives-ouvertes.fr/hal-00412288

High-dimensional probability density estimation with randomized ensembles of tree structured bayesian networks

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HAL-Univ-Nantes

Open Repository and Bibliography - Liège