Mixtures of Principal Component Analyzers

@inproceedings{Tipping1997MixturesOP,
  title={Mixtures of Principal Component Analyzers},
  author={Michael E. Tipping and Christopher M. Bishop},
  year={1997}
}
Principal component analysis (PCA) is a ubiquitous technique for data analysis but one whose effective application is restricted by its global linear character. While global nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data nonlinearity by a mixture of local PCA models. However, existing techniques are limited by the absence of a probabilistic formalism with an appropriate likelihood measure and so require an arbitrary choice of implementation strategy… CONTINUE READING

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