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Regular version of the site

Book chapter

Robust PLSA Performs Better Than LDA

P. 784-787.
Anna Potapenko, Konstantin Vorontsov.

In this paper we introduce a generalized learning algorithm for probabilistic topic models (PTM). Many known and new algorithms for PLSA, LDA, and SWB models can be obtained as its special cases by choosing a subset of the following “options”: regularization, sampling, update frequency, sparsing and robustness. We show that a robust topic model, which distinguishes specific, background and topic terms, doesn’t need Dirichlet regularization and provides controllably sparse solution.

In book

Edited by: P. Serdyukov, P. Braslavski, S. Kuznetsov et al. Vol. 7814. Springer, 2013.