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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 12/2018

26.04.2017 | Original Article

Self-organizing weighted incremental probabilistic latent semantic analysis

verfasst von: Ning Li, Wenjuan Luo, Kun Yang, Fuzhen Zhuang, Qing He, Zhongzhi Shi

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 12/2018

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Abstract

PLSA (Probabilistic Latent Semantic Analysis) is a popular topic modeling technique which has been widely applied to text mining applications to discover the underlying topics embedded in the data corpus. However, due to the variability of increasing data, it is necessary to discover the dynamic topics and process the large dataset incrementally. Moreover, PLSA models suffer from the problem of inferencing new documents. To overcome these problems, in this paper, we propose a novel Weighted Incremental PLSA algorithm called WIPLSA to dynamically discover topics and incrementally learn the topics from new documents. The experiments verify that the proposed WIPLSA could capture the dynamic topics hidden in the dynamic updating data corpus. Compared with PLSA, MAP PLSA and QB PLSA, WIPLSA performs better in perspexity on large dataset, which make it applicable for big data mining. In addition, WIPLSA has good performance in the application of document categorization.
Vorheriger Artikel A line symmetry based genetic clustering technique: encoding lines in chromosomes
Nächster Artikel Detection and localization of crowd behavior using a novel tracklet-based model

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Literatur
1.
2.
Yan Y, Chen L, Tjhi W-C (2013) Fuzzy semi-supervised co-clustering for text documents. Fuzzy Sets Syst. 215:74–89MathSciNetCrossRef
3.
Shehata S, Karray F, Kamel MS (2013) An efficient concept-based retrieval model for enhancing text retrieval quality. Knowl Inf Syst 1–24
4.
Freire A, Cacheda F, Formoso V, Carneiro V (2013) Analysis of performance evaluation techniques for large-scale information retrieval. Analyzing the Performance of Top-K Retrieval Algorithms, INVITED SPEAKER, p 2001
5.
Choo J, Lee C, Clarkson E, Liu Z, Lee H, Chau DHP, Li F, Kannan R, Stolper CD, Inouye D et al (2013) Visirr: Interactive visual information retrieval and recommendation for large-scale document data
6.
Mei Q, Zhai C (2001) A note on em algorithm for probabilistic latent semantic analysis. In: Proceedings of the International Conference on Information and Knowledge Management, CIKM
7.
Bai L, Liang J, Dang C, Cao F (2013) A novel fuzzy clustering algorithm with between-cluster information for categorical data. Fuzzy Sets Syst 215:55–73MathSciNetCrossRef
8.
Liu CL, Chang TH, Li HH (2013) Clustering documents with labeled and unlabeled documents using fuzzy semi-kmeans. Fuzzy Sets Syst
9.
Hakala K, Van Landeghem S, Salakoski T, Van de Peer Y, Ginter F (2013) Evex in st13: application of a large-scale text mining resource to event extraction and network construction. ACL 2013:26
10.
Zhou E, Zhong N, Li Y (2013) Extracting news blog hot topics based on the w2t methodology. World Wide Web, pp 1–28
11.
Wang X, Wang J (2013) A method of hot topic detection in blogs using n-gram model. J Softw 8:184–191CrossRef
12.
Steyvers M, Griffiths T (2007) Probabilistic topic models. Handb Latent Semantic Anal 427:424–440
13.
Blei DM, Lafferty JD (2006) Dynamic topic models. In: Proceedings of the 23rd international conference on Machine learning. ACM, pp 113–120
14.
Wang X, McCallum A (2006) Topics over time: a non-markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 424–433
15.
16.
Aggarwal CC, Zhai C (2012) Mining text data. Springer
17.
18.
Bolshakova E, Loukachevitch N, Nokel M (2013) Topic models can improve domain term extraction. In: Advances in Information Retrieval. Springer, pp 684–687
19.
Lin C, He Y, Everson R, Ruger S (2012) Weakly supervised joint sentiment-topic detection from text. IEEE Trans Knowl Data Eng 24:1134–1145CrossRef
20.
Deerwester SC, Dumais ST, Landauer TK, Furnas GW, Harshman RA (1990) Indexing by latent semantic analysis. JASIS 41:391–407CrossRef
21.
Hofmann T (1999) Probabilistic latent semantic indexing. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 50–57
22.
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022MATH
23.
Chaney AJB, Blei DM (2012) Visualizing topic models. In: ICWSM
24.
Zhai K, Boyd-Graber J, Asadi N, Alkhouja (2012) Mr. lda: a flexible large scale topic modeling package using variational inference in mapreduce. In: Proceedings of the 21st international conference on World Wide Web. ACM, pp 879–888
25.
Li N, Zhuang F, He Q, Shi Z (2012) Pplsa: Parallel probabilistic latent semantic analysis based on mapreduce. In: Intelligent Information Processing VI. Springer, pp 40–49
26.
Chien J-T, Wu M-S (2008) Adaptive bayesian latent semantic analysis. IEEE Trans Audio Speech Lang Process 16:198–207CrossRef
27.
Wu H, Wang Y, Cheng X (2008) Incremental probabilistic latent semantic analysis for automatic question recommendation. In: Proceedings of the 2008 ACM conference on Recommender systems. ACM, pp 99–106
28.
Tzu-Chuan Chou MCC (2008) Using incremental plsi for threshold-resilient online event analysis. IEEE Trans Knowl Data Eng 20:289–299CrossRef
29.
Hofmann T (2001) Unsupervised learning by probabilistic latent semantic analysis. Mach Learn 42:177–196CrossRef
30.
Surendran AC, Sra S (2006) Incremental aspect models for mining document streams. In: Knowledge Discovery in Databases: PKDD 2006. Springer, pp 633–640
31.
Wu H, Wang Y (2009) Incremental learning of triadic plsa for collaborative filtering. In: Active Media Technology. Springer, pp 81–92CrossRef
32.
Qian Y (2016) Context based approach to overlapping ambiguity resolution in chinese word segmentation. J Chongqing Technol Bus Univ (Nat Sci Edn) 20–24
Metadaten
Titel
Self-organizing weighted incremental probabilistic latent semantic analysis
verfasst von
Ning Li
Wenjuan Luo
Kun Yang
Fuzhen Zhuang
Qing He
Zhongzhi Shi
Publikationsdatum
26.04.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 12/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0681-9

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