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Relation between PLSA and NMF and implications

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SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 601 - 602

https://doi.org/10.1145/1076034.1076148

Published: 15 August 2005 Publication History

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Abstract

Non-negative Matrix Factorization (NMF, [5]) and Probabilistic Latent Semantic Analysis (PLSA, [4]) have been successfully applied to a number of text analysis tasks such as document clustering. Despite their different inspirations, both methods are instances of multinomial PCA [1]. We further explore this relationship and first show that PLSA solves the problem of NMF with KL divergence, and then explore the implications of this relationship.

References

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[2]

E. Gaussier, C. Goutte, K. Popat, and F. Chen. A hierarchical model for clustering and categorising documents. In Advances in Information Retrieval, Lecture Notes in Computer Science. Springer, 2002.

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C. Goutte, K. Yamada, and E. Gaussier. Aligning words using matrix factorisation. In ACL'04, 2004.

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T. Hofmann. Probabilistic latent semantic analysis. In UAI'99, pages 289--296. Morgan Kaufmann, 1999.

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D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401:788--791, 1999.

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D. D. Lee and H. S. Seung. Algorithms for non-negative matrix factorization. In NIPS'13. MIT Press, 2001.

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W. Xu, X. Liu, and Y. Gong. Document clustering based on non-negative matrix factorization. In SIGIR'03, 2003.

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Cited By

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Figuera PGarcía Bringas P(2024)Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and InsightsTechnologies10.3390/technologies1201000512:1(5)Online publication date: 3-Jan-2024
https://doi.org/10.3390/technologies12010005
Li XTyagi A(2023)Block-Active ADMM to Minimize NMF with Bregman DivergencesSensors10.3390/s2316722923:16(7229)Online publication date: 17-Aug-2023
https://doi.org/10.3390/s23167229
Hutchison PGeorge BGuragai B(2023)Application of Latent Semantic Analysis in Accounting ResearchJournal of Information Systems10.2308/ISYS-2022-01337:3(139-155)Online publication date: 25-Oct-2023
https://doi.org/10.2308/ISYS-2022-013
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Index Terms

Relation between PLSA and NMF and implications
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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Published In

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

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Published: 15 August 2005

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SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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Figuera PGarcía Bringas P(2024)Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and InsightsTechnologies10.3390/technologies1201000512:1(5)Online publication date: 3-Jan-2024
https://doi.org/10.3390/technologies12010005
Li XTyagi A(2023)Block-Active ADMM to Minimize NMF with Bregman DivergencesSensors10.3390/s2316722923:16(7229)Online publication date: 17-Aug-2023
https://doi.org/10.3390/s23167229
Hutchison PGeorge BGuragai B(2023)Application of Latent Semantic Analysis in Accounting ResearchJournal of Information Systems10.2308/ISYS-2022-01337:3(139-155)Online publication date: 25-Oct-2023
https://doi.org/10.2308/ISYS-2022-013
Wang PVan hamme H(2023)Benefits of pre-trained mono- and cross-lingual speech representations for spoken language understanding of Dutch dysarthric speechEURASIP Journal on Audio, Speech, and Music Processing10.1186/s13636-023-00280-z2023:1Online publication date: 7-Apr-2023
https://doi.org/10.1186/s13636-023-00280-z
Carbonetto PLuo KSarkar AHung ATayeb KPott SStephens M(2023)GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membershipGenome Biology10.1186/s13059-023-03067-924:1Online publication date: 19-Oct-2023
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Park DKoo POh HYoon M(2023)Recent Research Trend Analysis for the Journal of Society of Korea Industrial and Systems Engineering Using Topic ModelingJournal of Society of Korea Industrial and Systems Engineering10.11627/jksie.2023.46.3.17046:3(170-185)Online publication date: 30-Sep-2023
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Rashid JKim JNaseem U(2023)Incorporating Embedding to Topic Modeling for More Effective Short Text AnalysisCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587316(73-76)Online publication date: 30-Apr-2023
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Hoseinipour SAminghafari MMohammadpour A(2023)Orthogonal parametric non-negative matrix tri-factorization with α-divergence for co-clusteringExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120680231:COnline publication date: 30-Nov-2023
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Haddock JWill TVendrow JZhang RMolitor DNeedell DGao MSadovnik E(2023)Neural nonnegative matrix factorization for hierarchical multilayer topic modelingSampling Theory, Signal Processing, and Data Analysis10.1007/s43670-023-00077-322:1Online publication date: 19-Dec-2023
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Sharma ASharma VJaiswal MWang HJayakody DBasnayaka CMuthanna A(2022)Recent Trends in AI-Based Intelligent SensingElectronics10.3390/electronics1110166111:10(1661)Online publication date: 23-May-2022
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PLSA-based image auto-annotation: constraining the latent space

RPLSA: A novel updating scheme for Probabilistic Latent Semantic Analysis

Improving NMF clustering by leveraging contextual relationships among words

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