ML Anthology

Dimension Reduction: A Guided Tour

Christopher J. C. Burges
FTML 2010
doi:10.1561/2200000002 /ftml/2010/burges2010ftml-dimension/

Abstract

We give a tutorial overview of several geometric methods for dimension reduction. We divide the methods into projective methods and methods that model the manifold on which the data lies. For projective methods, we review projection pursuit, principal component analysis (PCA), kernel PCA, probabilistic PCA, canonical correlation analysis, oriented PCA, and several techniques for sufficient dimension reduction. For the manifold methods, we review multidimensional scaling (MDS), landmark MDS, Isomap, locally linear embedding, Laplacian

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Cite

Text

Burges. "Dimension Reduction: A Guided Tour." Foundations and Trends in Machine Learning, 2010. doi:10.1561/2200000002

Markdown

[Burges. "Dimension Reduction: A Guided Tour." Foundations and Trends in Machine Learning, 2010.](https://mlanthology.org/ftml/2010/burges2010ftml-dimension/) doi:10.1561/2200000002

BibTeX

@article{burges2010ftml-dimension,
  title     = {{Dimension Reduction: A Guided Tour}},
  author    = {Burges, Christopher J. C.},
  journal   = {Foundations and Trends in Machine Learning},
  year      = {2010},
  doi       = {10.1561/2200000002},
  volume    = {2},
  url       = {https://mlanthology.org/ftml/2010/burges2010ftml-dimension/}
}