%0 Conference Proceedings
%T PPLSA: Parallel Probabilistic Latent Semantic Analysis Based on MapReduce
%+ Graduate School of the Chinese Academy of Sciences (GSCAS)
%+ Key Laboratory of Intelligent Information Processing, Institute of Computing Technology [Beijing]
%A Li, Ning
%A Zhuang, Fuzhen
%A He, Qing
%A Shi, Zhongzhi
%Z Part 2: Machine Learning
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 7th International Conference on Intelligent Information Processing (IIP)
%C Guilin, China
%Y Zhongzhi Shi
%Y David Leake
%Y Sunil Vadera
%I Springer
%3 Intelligent Information Processing VI
%V AICT-385
%P 40-49
%8 2012-10-12
%D 2012
%R 10.1007/978-3-642-32891-6_8
%K Probabilistic Latent Semantic Analysis
%K MapReduce
%K EM
%K Parallel
%Z Computer Science [cs]Conference papers
%X PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement.
%G English
%Z TC 12
%2 https://inria.hal.science/hal-01524958/document
%2 https://inria.hal.science/hal-01524958/file/978-3-642-32891-6_8_Chapter.pdf
%L hal-01524958
%U https://inria.hal.science/hal-01524958
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC12
%~ IFIP-IIP
%~ IFIP-AICT-385