PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

Ning Li; Fuzhen Zhuang; Qing He; Zhongzhi Shi

doi:10.1007/978-3-642-32891-6_8

PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

Ning Li^*
, Fuzhen Zhuang
, Qing He
, Zhongzhi Shi

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Intelligent Information Processing VI - 7th IFIP TC 12 International Conference, IIP 2012, Proceedings
Pages	40-49
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-32891-6_8
State	Published - 2012
Externally published	Yes
Event	7th IFIP International Conference on Intelligent Information Processing, IIP 2012 - Guilin, China Duration: 12 Oct 2012 → 15 Oct 2012

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	385 AICT
ISSN (Print)	1868-4238

Conference

Conference	7th IFIP International Conference on Intelligent Information Processing, IIP 2012
Country/Territory	China
City	Guilin
Period	12/10/12 → 15/10/12

Keywords

EM
MapReduce
Parallel
Probabilistic Latent Semantic Analysis

Access to Document

10.1007/978-3-642-32891-6_8

Cite this

@inproceedings{65a7c8b8d8bd49499bc9e6e4ea6817bc,

title = "PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce",

abstract = "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.",

keywords = "EM, MapReduce, Parallel, Probabilistic Latent Semantic Analysis",

author = "Ning Li and Fuzhen Zhuang and Qing He and Zhongzhi Shi",

year = "2012",

doi = "10.1007/978-3-642-32891-6\_8",

language = "英语",

isbn = "9783642328909",

series = "IFIP Advances in Information and Communication Technology",

pages = "40--49",

booktitle = "Intelligent Information Processing VI - 7th IFIP TC 12 International Conference, IIP 2012, Proceedings",

note = "7th IFIP International Conference on Intelligent Information Processing, IIP 2012 ; Conference date: 12-10-2012 Through 15-10-2012",

}

Li, N, Zhuang, F, He, Q & Shi, Z 2012, PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce. in Intelligent Information Processing VI - 7th IFIP TC 12 International Conference, IIP 2012, Proceedings. IFIP Advances in Information and Communication Technology, vol. 385 AICT, pp. 40-49, 7th IFIP International Conference on Intelligent Information Processing, IIP 2012, Guilin, China, 12/10/12. https://doi.org/10.1007/978-3-642-32891-6_8

PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce. / Li, Ning; Zhuang, Fuzhen; He, Qing et al.
Intelligent Information Processing VI - 7th IFIP TC 12 International Conference, IIP 2012, Proceedings. 2012. p. 40-49 (IFIP Advances in Information and Communication Technology; Vol. 385 AICT).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - PPLSA

T2 - 7th IFIP International Conference on Intelligent Information Processing, IIP 2012

AU - Li, Ning

AU - Zhuang, Fuzhen

AU - He, Qing

AU - Shi, Zhongzhi

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - EM

KW - MapReduce

KW - Parallel

KW - Probabilistic Latent Semantic Analysis

UR - https://www.scopus.com/pages/publications/84870697816

U2 - 10.1007/978-3-642-32891-6_8

DO - 10.1007/978-3-642-32891-6_8

M3 - 会议稿件

AN - SCOPUS:84870697816

SN - 9783642328909

T3 - IFIP Advances in Information and Communication Technology

SP - 40

EP - 49

BT - Intelligent Information Processing VI - 7th IFIP TC 12 International Conference, IIP 2012, Proceedings

Y2 - 12 October 2012 through 15 October 2012

ER -

PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

Abstract

Publication series

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Keywords

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