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- W2235651158 abstract "Latent Dirichlet Allocation (LDA) represents perhaps the most famous topic model, employed in many different contexts in Computer Science. The wide success of LDA is due to the effectiveness of this model in dealing with large datasets, the competitive performances obtained on several tasks (e.g. classification, clustering), and the interpretability of the solution provided. Learning the LDA from training data usually requires to employ iterative optimization techniques such as the Expectation-Maximization, for which the choice of a good initialization is of crucial importance to reach an optimal solution. However, even if some clever solutions have been proposed, in practical applications this issue is typically disregarded, and the usual solution is to resort to random initialization." @default.
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- W2235651158 date "2015-01-01" @default.
- W2235651158 modified "2023-09-27" @default.
- W2235651158 title "Robust Initialization for Learning Latent Dirichlet Allocation" @default.
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