Revisiting probabilistic latent semantic analysis: extensions, challenges and insights

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dc.contributor.authorFiguera, Pau
dc.contributor.authorGarcía Bringas, Pablo
dc.date.accessioned2025-05-21T07:34:06Z
dc.date.available2025-05-21T07:34:06Z
dc.date.issued2024-01-03
dc.date.updated2025-05-21T07:34:06Z
dc.description.abstractThis manuscript provides a comprehensive exploration of Probabilistic latent semantic analysis (PLSA), highlighting its strengths, drawbacks, and challenges. The PLSA, originally a tool for information retrieval, provides a probabilistic sense for a table of co-occurrences as a mixture of multinomial distributions spanned over a latent class variable and adjusted with the expectation–maximization algorithm. The distributional assumptions and the iterative nature lead to a rigid model, dividing enthusiasts and detractors. Those drawbacks have led to several reformulations: the extension of the method to normal data distributions and a non-parametric formulation obtained with the help of Non-negative matrix factorization (NMF) techniques. Furthermore, the combination of theoretical studies and programming techniques alleviates the computational problem, thus making the potential of the method explicit: its relation with the Singular value decomposition (SVD), which means that PLSA can be used to satisfactorily support other techniques, such as the construction of Fisher kernels, the probabilistic interpretation of Principal component analysis (PCA), Transfer learning (TL), and the training of neural networks, among others. We also present open questions as a practical and theoretical research window.en
dc.identifier.citationFiguera, P., & García Bringas, P. (2024). Revisiting probabilistic latent semantic analysis: extensions, challenges and insights [Review of Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and Insights]. Technologies, 12(1). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/TECHNOLOGIES12010005
dc.identifier.doi10.3390/TECHNOLOGIES12010005
dc.identifier.eissn2227-7080
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2795
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rights© 2024 by the authors
dc.subject.otherNonnegative matrix factorization
dc.subject.otherProbabilistic latent semantic analysis
dc.subject.otherProbabilistic semantic indexing
dc.subject.otherSingular value decomposition
dc.titleRevisiting probabilistic latent semantic analysis: extensions, challenges and insightsen
dc.typereview
dcterms.accessRightsopen access
oaire.citation.issue1
oaire.citation.titleTechnologies
oaire.citation.volume12
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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