Efficiently approximating Markov tree bagging for high-dimensional density estimation
published: Oct. 3, 2011, recorded: September 2011, views: 2364
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We consider algorithms for generating Mixtures of Bagged Markov Trees, for density estimation. In problems deﬁned over many variables and when few observations are available, those mixtures generally outperform a single Markov tree maximizing the data likelihood, but are far more expensive to compute. In this paper, we describe new algorithms for approximating such models, with the aim of speeding up learning without sacriﬁcing accuracy. More speciﬁcally, we propose to use a ﬁltering step obtained as a by-product from computing a ﬁrst Markov tree, so as to avoid considering poor candidate edges in the subsequently generated trees. We compare these algorithms (on synthetic data sets) to Mixtures of Bagged Markov Trees, as well as to a single Markov tree derived by the classical Chow-Liu algorithm and to a recently proposed randomized scheme used for building tree mixtures.
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