Dimension Reduction A Guided Tour Chris J C Burges

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Dimension Reduction: A Guided Tour

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/FnT_dimensionReduction.pdf

c 2010 C. J. C. Burges DOI: 10.1561/2200000002 Dimension Reduction: A Guided Tour Christopher J. C. Burges Microsoft Research, One Microsoft Way, Redmond, WA 98052-6399, USA, [email protected] Abstract We give a tutorial overview of several foundational methods for dimen-sion reduction. We divide the methods into projective methods andCited by: 251

Dimension Reduction: A Guided Tour (Foundations and Trends ...

https://www.amazon.com/Dimension-Reduction-Foundations-Machine-Learning/dp/1601983786

Jul 20, 2010 · Dimension Reduction: A Guided Tour (Foundations and Trends(r) in Machine Learning) Paperback – July 20, 2010 by Christopher J. C. Burges (Author) See all formats and editions Hide other formats and editionsCited by: 251

DRAFT

https://www.seas.harvard.edu/courses/cs281/papers/burges-2009.pdf

Dimension Reduction: A Guided TourDRAFT Christopher J.C. Burges1 1 Microsoft Research, One Microsoft Way, Redmond, WA 98052-6399, USA. MSR Tech Report MSR-TR-2009-2013 Abstract We give a tutorial overview of several geometric methods for dimension

now publishers - Dimension Reduction: A Guided Tour

https://www.nowpublishers.com/article/Details/MAL-002

Dimension Reduction: A Guided Tour covers many well-known, and some less well-known, methods for dimension reduction for which the inferred variables are continuous. It describes the mathematics and key ideas underlying the methods, and provides some links to the literature for those interested in pursuing a topic further.Cited by: 251

CiteSeerX — Dimension Reduction: A Guided Tour

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.180.861

@MISC{Burges10dimensionreduction:, author = {Christopher J. C. Burges}, title = { Dimension Reduction: A Guided Tour}, year = {2010}} Share. OpenURL . Abstract. We give a tutorial overview of several foundational methods for dimension reduction. We divide the methods into projective methods and methods that model the manifold on which the data ...

(PDF) Dimension Reduction: A Guided Tour

https://www.researchgate.net/publication/220416606_Dimension_Reduction_A_Guided_Tour

PCA is a classical orthogonal dimension reduction method (Burges, 2010) to reduce the dimension from multi-spectral time series. PCA is known as a spectral decomposition (or transformation) method ...

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