Representing Complex Data Using Localized Principal Components with Application to Astronomical Data

@inproceedings{Einbeck2008RepresentingCD,
  title={Representing Complex Data Using Localized Principal Components with Application to Astronomical Data},
  author={Jochen Einbeck and Ludger Evers and Coryn A. L. Bailer-Jones and Department of Mathematical Sciences and Durham University and Department of Mathematics and University of Bristol and Max Planck Institute for Radio Astronomy and V. Heidelberg},
  year={2008}
}
Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: “nonlinear”, “branched”, “disconnected”, “bended”, “curved”, “heterogeneous”, or, more general, “complex”. In these cases, simple principal component analysis (PCA) as a tool for dimension reduction can fail badly. Of the many alternative approaches proposed so far, local approximations of PCA are among the most promising. This paper will give a short review of… CONTINUE READING

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