Robust PLSA performs better than LDA

Authors:
Anna Potapenko;Konstantin Vorontsov
Affiliations:
MSU, CMC, Moscow, Russia;MIPT, Moscow, Russia,CC RAS, Moscow, Russia
Venue:
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Year:
2013

Citing 3
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Abstract

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.