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dc.contributor.authorMeila, Marinaen_US
dc.date.accessioned2004-10-08T20:37:13Z
dc.date.available2004-10-08T20:37:13Z
dc.date.issued1999-01-01en_US
dc.identifier.otherAIM-1652en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/6676
dc.description.abstractChow and Liu introduced an algorithm for fitting a multivariate distribution with a tree (i.e. a density model that assumes that there are only pairwise dependencies between variables) and that the graph of these dependencies is a spanning tree. The original algorithm is quadratic in the dimesion of the domain, and linear in the number of data points that define the target distribution $P$. This paper shows that for sparse, discrete data, fitting a tree distribution can be done in time and memory that is jointly subquadratic in the number of variables and the size of the data set. The new algorithm, called the acCL algorithm, takes advantage of the sparsity of the data to accelerate the computation of pairwise marginals and the sorting of the resulting mutual informations, achieving speed ups of up to 2-3 orders of magnitude in the experiments.en_US
dc.format.extent1375477 bytes
dc.format.extent434859 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAIM-1652en_US
dc.titleAn Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High Dimensional Sparse Dataen_US


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