Andere Kunden interessierten sich auch für
![Latent Variable Regression Analysis with Missing Covariates]( "Latent Variable Regression Analysis with Missing Covariates")
Latent Variable Regression Analysis with Missing Covariates38,99 €![Latent Class Analysis of Survey Error]( "Latent Class Analysis of Survey Error")
Latent Class Analysis of Survey Error137,99 €![Latent Class Analysis]( "Latent Class Analysis")
Latent Class Analysis40,99 €![Issues on the Estimation of Latent Variable and Latent Class Models]( "Issues on the Estimation of Latent Variable and Latent Class Models")
Issues on the Estimation of Latent Variable and Latent Class Models29,99 €![Latent Markov Models for Longitudinal Data]( "Latent Markov Models for Longitudinal Data")
Latent Markov Models for Longitudinal Data219,99 €![Robust Latent Feature Learning for Incomplete Big Data]( "Robust Latent Feature Learning for Incomplete Big Data")
Robust Latent Feature Learning for Incomplete Big Data39,99 €![PLS-PM Latent Unit Grouping and Classification with Kohonen Networks]( "PLS-PM Latent Unit Grouping and Classification with Kohonen Networks")
PLS-PM Latent Unit Grouping and Classification with Kohonen Networks61,99 €
High Quality Content by WIKIPEDIA articles! 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. PLSA evolved from Latent semantic analysis, adding a sounder probabilistic model. PLSA has applications in information retrieval and filtering, natural language processing, machine learning from text, and related areas. It was introduced in 1999 by Jan Puzicha and Thomas Hofmann, and it is related to non-negative matrix factorization.