Random Projections for Support Vector Machines
Year: 2012
Type: preprint
Abstract: Let X be a data matrix of rank ρ, representing n points in d-dimensional space. 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 ɛ-relative error, ensuring comparable generalization as in the original space. We present extensive experiments with real and synthetic data to support our theory. 1
Source: arXiv (Cornell University)
Institutions: Rensselaer Polytechnic Institute, IBM (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2104624965