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'''Probabilistic latent semantic analysis''' ('''PLSA'''), also known as '''probabilistic latent semantic indexing''' ('''PLSI''', especially in information retrieval circles) is a [[statistical technique]] for the analysis of two-mode and co-occurrence data. In effect, one can derive a low-dimensional representation of the observed variables in terms of their affinity to certain hidden variables, just as in [[latent semantic analysis]], from which PLSA evolved.
Compared to standard [[latent semantic analysis]] which stems from [[linear algebra]] and downsizes the occurrence tables (usually via a [[singular value decomposition]]), probabilistic latent semantic analysis is based on a mixture decomposition derived from a [[latent class model]].
==Model==
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