• DOI:10.1016/S0167-9473(02)00183-4
Corpus ID: 7169099

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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