2014 | OriginalPaper | Buchkapitel
Online Learning for Two Novel Latent Topic Models
verfasst von : Ali Shojaee Bakhtiari, Nizar Bouguila
Erschienen in: Information and Communication Technology
Verlag: Springer Berlin Heidelberg
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Latent topic models have proven to be an efficient tool for modeling multitopic count data. One of the most well-known models is the latent Dirichlet allocation (LDA). In this paper we propose two improvements for LDA using generalized Dirichlet and Beta-Liouville prior assumptions. Moreover, we apply an online learning approach for both introduced approaches. We choose a challenging application namely natural scene classification for comparison and evaluation purposes.