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• W2113111165 abstract "The problem of approximating a given probability distribution using a simpler distribution plays an important role in several areas of machine learning, for example variational inference and classification. Within this context, we consider the task of learning a mixture of tree distributions. Although mixtures of trees can be learned by minimizing the KL-divergence using an EM algorithm, its success depends heavily on the initialization. We propose an efficient strategy for obtaining a good initial set of trees that attempts to cover the entire observed distribution by minimizing the α-divergence with α = ∞. We formulate the problem using the fractional covering framework and present a convergent sequential algorithm that only relies on solving a convex program at each iteration. Compared to previous methods, our approach results in a significantly smaller mixture of trees that provides similar or better accuracies. We demonstrate the usefulness of our approach by learning pictorial structures for face recognition." @default.
• W2113111165 created "2016-06-24" @default.
• W2113111165 creator A5022356850 @default.
• W2113111165 creator A5051658526 @default.
• W2113111165 date "2009-12-07" @default.
• W2113111165 modified "2023-09-24" @default.
• W2113111165 title "Learning a Small Mixture of Trees" @default.
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