%0 Conference Proceedings
%T Online Learning for Two Novel Latent Topic Models
%+ Concordia University [Montreal]
%A Bakhtiari, Ali, Shojaee
%A Bouguila, Nizar
%Z Part 1: Information & Communication Technology-EurAsia Conference 2014, ICT-EurAsia 2014
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 2nd Information and Communication Technology - EurAsia Conference (ICT-EurAsia)
%C Bali, Indonesia
%Y David Hutchison
%Y Takeo Kanade
%Y Bernhard Steffen
%Y Demetri Terzopoulos
%Y Doug Tygar
%Y Gerhard Weikum
%Y Linawati
%Y Made Sudiana Mahendra
%Y Erich J. Neuhold
%Y A Min Tjoa
%Y Ilsun You
%Y Josef Kittler
%Y Jon M. Kleinberg
%Y Alfred Kobsa
%Y Friedemann Mattern
%Y John C. Mitchell
%Y Moni Naor
%Y Oscar Nierstrasz
%Y C. Pandu Rangan
%I Springer
%3 Information and Communication Technology
%V LNCS-8407
%P 286-295
%8 2014-04-14
%D 2014
%R 10.1007/978-3-642-55032-4_28
%K Generalized Dirichlet
%K Beta-Liouville
%K online learning
%K Latent model
%K variational learning
%K count data
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 5
%Z TC 8
%2 https://inria.hal.science/hal-01397223/document
%2 https://inria.hal.science/hal-01397223/file/978-3-642-55032-4_28_Chapter.pdf
%L hal-01397223
%U https://inria.hal.science/hal-01397223
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-TC8
%~ IFIP-ICT-EURASIA
%~ IFIP-LNCS-8407