On the equivalence between algorithms for non-negative matrix factorization and latent Dirichlet allocation

dc.creator	Faleiros, Thiago de Paulo
dc.creator	Lopes, Alneu de Andrade
dc.date.accessioned	2016-10-20T19:05:11Z
dc.date.accessioned	2018-07-04T17:12:00Z
dc.date.available	2016-10-20T19:05:11Z
dc.date.available	2018-07-04T17:12:00Z
dc.date.created	2016-10-20T19:05:11Z
dc.date.issued	2016-04
dc.identifier	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, XXIV, 2016, Bruges.
dc.identifier	9782875870278
dc.identifier	http://www.producao.usp.br/handle/BDPI/51059
dc.identifier	https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-162.pdf
dc.identifier.uri	http://repositorioslatinoamericanos.uchile.cl/handle/2250/1646016
dc.description.abstract	LDA (Latent Dirichlet Allocation ) and NMF (Non-negative Matrix Factorization) are two popular techniques to extract topics in a textual document corpus. This paper shows that NMF with Kullback-Leibler divergence approximate the LDA model under a uniform Dirichlet prior, therefore the comparative analysis can be useful to elucidate the implementation of variational inference algorithm for LDA.
dc.language	eng
dc.publisher	European Neural Network Society - ENNS
dc.publisher	Bruges
dc.relation	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, XXIV
dc.rights	closedAccess
dc.title	On the equivalence between algorithms for non-negative matrix factorization and latent Dirichlet allocation
dc.type	Actas de congresos