Paula Harder, Venkatesh Ramesh, et al.
EGU 2023
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 ε-relative error, ensuring comparable generalization as in the original space. We present extensive experiments with real and synthetic data to support our theory.
Paula Harder, Venkatesh Ramesh, et al.
EGU 2023
Ran Iwamoto, Kyoko Ohara
ICLC 2023
Paul G. Comba
Journal of the ACM
Hong-linh Truong, Maja Vukovic, et al.
ICDH 2024