Semi-supervised Gaussian mixture modelling with a missing-data mechanism in \textsf{R}

DOI10.1111/ANZS.12413MaRDI QIDQ6581424

Geoffrey J. McLachlan, Ziyang Lyu, Ryan C. Thompson, Daniel Ahfock

Publication date: 30 July 2024

Published in: Australian \& New Zealand Journal of Statistics (Search for Journal in Brave)

zbMATH Keywords

entropymixture modelsemi-supervised learningBayes' rulepartially classified sample

Mathematics Subject Classification ID

Parametric inference (62Fxx) Artificial intelligence (68Txx) Multivariate analysis (62Hxx)

Cites Work

• Title not available (Why is that?)
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• Texture classification and segmentation using wavelet packet frame and Gaussian mixture model
• Learning from partially supervised data using mixture models and belief functions
• An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classified
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Clarifying missing at random and related definitions, and implications when coupled with exchangeability: Table 1.
• The EM Algorithm and Extensions, 2E
• Iterative Reclassification Procedure for Constructing an Asymptotically Optimal Rule of Allocation in Discriminant Analysis
• Inference and missing data
• Estimating the Linear Discriminant Function from Initial Samples Containing a Small Number of Unclassified Observations
• The efficiency of a linear discriminant function based on unclassified initial samples
• Finite mixture models

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