Online Learning for Two Novel Latent Topic Models

Abstract : 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.
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Ali Bakhtiari, Nizar Bouguila. Online Learning for Two Novel Latent Topic Models. 2nd Information and Communication Technology - EurAsia Conference (ICT-EurAsia), Apr 2014, Bali, Indonesia. pp.286-295, ⟨10.1007/978-3-642-55032-4_28⟩. ⟨hal-01397223⟩

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