Probabilistic latent semantic analysis: Difference between revisions

Revision as of 13:30, 20 June 2022

Revision as of 09:03, 28 June 2022

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PLSA may be used in a discriminative setting, via [[Fisher kernel]]s.<ref>Thomas Hofmann, [https://papers.nips.cc/paper/1654-learning-the-similarity-of-documents-an-information-geometric-approach-to-document-retrieval-and-categorization.pdf ''Learning the Similarity of Documents : an information-geometric approach to document retrieval and categorization''], [[Advances in Neural Information Processing Systems]] 12, pp-914-920, [[MIT Press]], 2000</ref>

PLSA has applications in [[information retrieval]] and [[information filtering|filtering]], [[natural language processing]], [[machine learning]] from text, [[bioinformatics]],<ref>{{Cite conference|chapter=Enhanced probabilistic latent semantic analysis with weighting schemes to predict genomic annotations|conference=The 13th IEEE International Conference on BioInformatics and BioEngineering|last1=Pinoli|first1=Pietro|last2=et|first2=al.|title= Proceedings of IEEE BIBE 2013 |date=2013|publisher=IEEE|pages=1–4|language=en|doi=10.1109/BIBE.2013.6701702|isbn=978-147993163-7}}

</ref>, and related areas.

It is reported that the [[aspect model]] used in the probabilistic latent semantic analysis has severe [[overfitting]] problems.<ref>{{cite journal|title=Latent Dirichlet Allocation|journal=Journal of Machine Learning Research|year=2003|first=David M.|last=Blei|author2=Andrew Y. Ng |author3=Michael I. Jordan |volume=3|pages=993–1022|url=http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf|doi=10.1162/jmlr.2003.3.4-5.993}}</ref>