Two-way Poisson mixture models for simultaneous document classification and word clustering

DOI10.1016/j.csda.2004.07.013zbMath1429.62253OpenAlexW2117428599MaRDI QIDQ959145

Hongyuan Zha, Jia Li

Publication date: 11 December 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2004.07.013

zbMATH Keywords

document classificationword clusteringmixture of Poisson distributionssimultaneous classification and clusteringtwo-way mixture model

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10)

Related Items

Model based clustering of customer choice data ⋮ On the distribution of posterior probabilities in finite mixture models with application in clustering ⋮ Interpoint Distance Test of Homogeneity for Multivariate Mixture Models ⋮ A finite mixture approach to joint clustering of individuals and multivariate discrete outcomes ⋮ Biclustering multivariate discrete longitudinal data ⋮ Initializing the EM algorithm in Gaussian mixture models with an unknown number of components ⋮ Clustering microarray data using model-based doubleK-means ⋮ Clustering Discrete Choice Data ⋮ 2nd special issue on matrix computations and statistics ⋮ Finite mixture models and model-based clustering ⋮ Parameter estimation of Poisson mixture with automated model selection through BYY harmony learning ⋮ Hierarchical mixture models for biclustering in microarray data ⋮ Two‐way Gaussian mixture models for high dimensional classification ⋮ Editorial: Advances in mixture models

Uses Software

• Bow

Cites Work

• 10.1162/jmlr.2003.3.4-5.993
• Unsupervised learning by probabilistic latent semantic analysis
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Two-way Poisson mixture models for simultaneous document classification and word clustering