Author
Paul, Saurabh
Other Contributors
Drineas, Petros; Magdon-Ismail, Malik; Zaki, Mohammed J., 1971-;
Date Issued
2012-12
Subject
Computer science
Degree
MS;
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.; Attribution-NonCommercial-NoDerivs 3.0 United States
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
Department
Dept. of Computer Science;
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection;
Access
CC BY-NC-ND. Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. No commercial use or derivatives are permitted without the explicit approval of the author.;