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. © 2013 Springer-Verlag.
CITATION STYLE
Potapenko, A., & Vorontsov, K. (2013). Robust PLSA performs better than LDA. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7814 LNCS, pp. 784–787). https://doi.org/10.1007/978-3-642-36973-5_84
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