Random projections for support vector machines
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Authors
Paul, Saurabh
Issue Date
2012-12
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Computer science
Alternative Title
Abstract
Let X ∈ R n×d be a data matrix of rank ρ, representing n points in R d . The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X. We prove that, with high probability, the margin and minimum enclosing ball in the feature space are preserved to within -relative error, ensuring comparable generalization as in the original space. We present extensive experiments on synthetic and real-world datasets in support of the theory.
Description
December 2012
School of Science
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY