[PDF.94pd] Dimension Reduction: A Guided Tour (Foundations and Trends(r) in Machine Learning)

Dimension Reduction: A Guided Tour (Foundations and Trends(r) in Machine Learning)

[PDF.kb24] Dimension Reduction: A Guided Tour (Foundations and Trends(r) in Machine Learning)

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 | #922726 in Books |  Now Publishers Inc |  2010-07-20 | Original language:English | PDF # 1 |  9.21 x.22 x6.14l,.35 | File type: PDF | 106 pages

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||About the Author|Burges is Distinguished Member of Technical Staff at Lucent Technologies, Bell Laboratories.

Dimension reduction is the mapping of data to a lower dimensional space such that uninformative variance in the data is discarded, or such that a subspace in which the data lives is detected. Dimension reduction has a long history as a method for data visualization, and for extracting key low dimensional features (for example, the two-dimensional orientation of an object, from its high dimensional image representation). In some cases the desired low dimensional features ...

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