Modelling high-dimensional data by mixtures of factor analyzers

@article{McLachlan2003ModellingHD,
  title={Modelling high-dimensional data by mixtures of factor analyzers},
  author={G. McLachlan and D. Peel and Richard Bean},
  journal={Comput. Stat. Data Anal.},
  year={2003},
  volume={41},
  pages={379-388}
}
  • G. McLachlan, D. Peel, Richard Bean
  • Published 2003
  • Mathematics, Computer Science
  • Comput. Stat. Data Anal.
  • We focus on mixtures of factor analyzers from the perspective of a method for model-based density estimation from high-dimensional data, and hence for the clustering of such data. This approach enables a normal mixture model to be fitted to a sample of n data points of dimension p, where p is large relative to n. The number of free parameters is controlled through the dimension of the latent factor space. By working in this reduced space, it allows a model for each component-covariance matrix… CONTINUE READING
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    References

    SHOWING 1-10 OF 32 REFERENCES
    Variational Inference for Bayesian Mixtures of Factor Analysers
    • 436
    • PDF
    On using Principal Components before Separating a Mixture of Two Multivariate Normal Distributions
    • 284
    Coupled two-way clustering analysis of gene microarray data.
    • G. Getz, E. Levine, E. Domany
    • Biology, Physics
    • Proceedings of the National Academy of Sciences of the United States of America
    • 2000
    • 869
    • PDF
    Mixtures of Probabilistic Principal Component Analyzers
    • 1,654
    • PDF
    Latent Variable Models
    • C. Bishop
    • Mathematics, Computer Science
    • Learning in Graphical Models
    • 1998
    • 94
    Mixture Density Estimation
    • 245
    • PDF
    Bayesian Analysis of Mixtures of Factor Analyzers
    • 40
    • PDF
    Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays.
    • U. Alon, N. Barkai, +4 authors A. Levine
    • Biology, Medicine
    • Proceedings of the National Academy of Sciences of the United States of America
    • 1999
    • 4,060
    • PDF