Probabilistic latent semantic analysis: Difference between revisions

Revision as of 01:47, 20 October 2016

Revision as of 01:48, 20 October 2016

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==Extensions ==

* Hierarchical extensions:

** Asymmetric: MASHA ("Multinomial ASymmetric Hierarchical Analysis") <ref>Alexei Vinokourov and Mark Girolami, [http://citeseer.ist.psu.edu/rd/30973750,455249,1,0.25,Download/http://citeseer.ist.psu.edu/cache/papers/cs/22961/http:zSzzSzcis.paisley.ac.ukzSzvino-ci0zSzvinokourov_masha.pdf/vinokourov02probabilistic.pdf A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections], in ''Information Processing and Management'', 2002</ref>

** Symmetric: HPLSA ("Hierarchical Probabilistic Latent Semantic Analysis") <ref>Eric Gaussier, Cyril Goutte, Kris Popat and Francine Chen,

[http://www.xrce.xerox.com/Research-Development/Publications/2002-004 A Hierarchical Model for Clustering and Categorising Documents], in "Advances in Information Retrieval -- Proceedings of the 24th [[Information Retrieval Specialist Group|BCS-IRSG]] European Colloquium on IR Research (ECIR-02)", 2002</ref>

* Generative models: The following models have been developed to address an often-criticized shortcoming of PLSA, namely that it is not a proper generative model for new documents.

** [[Latent Dirichlet allocation]] -– adds a [[Dirichlet distribution|Dirichlet]] prior on the per-document topic distribution

* Higher-order data: Although this is rarely discussed in the scientific literature, PLSA extends naturally to higher order data (three modes and higher), i.e. it can model co-occurrences over three or more variables. In the symmetric formulation above, this is done simply by adding conditional probability distributions for these additional variables. This is the probabilistic analogue to non-negative tensor factorisation.

==History==

This is an example of a [[latent class model]] (see references therein), and it is related <ref>Chris Ding, Tao Li, Wei Peng (2006). "[http://www.aaai.org/Papers/AAAI/2006/AAAI06-055.pdf Nonnegative Matrix Factorization and Probabilistic Latent Semantic Indexing: Equivalence Chi-Square Statistic, and a Hybrid Method. AAAI 2006" ]</ref><ref>Chris Ding, Tao Li, Wei Peng (2008). "[http://www.sciencedirect.com/science/article/pii/S0167947308000145 On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing"]</ref> to [[non-negative matrix factorization]]. The present terminology was coined in 1999 by [[Thomas Hofmann]].<ref>Thomas Hofmann, [http://www.cs.brown.edu/~th/papers/Hofmann-SIGIR99.pdf ''Probabilistic Latent Semantic Indexing''], Proceedings of the Twenty-Second Annual International [[Special Interest Group on Information Retrieval|SIGIR]] Conference on Research and Development in [[Information Retrieval]] (SIGIR-99), 1999</ref>

== References and notes ==

{{reflist}}

== See also ==

* [[Compound term processing]]

* [[Latent Dirichlet allocation]]

* [[Latent semantic analysis]]

* [[Pachinko allocation]]

* [[Vector space model]]

== References and notes ==

{{reflist}}

==External links==