High-dimensional probability density estimation with randomized ensembles of tree structured bayesian networks
Year: 2008
Type: conference-paper
Abstract: In 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.
Source: ORBi (University of Liège)
Authors: Ammar, Sourour, Leray, Philippe, Defourny, Boris, Wehenkel, Louis
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1584379801