Cristina Cornelio, Judy Goldsmith, et al.
JAIR
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.
Cristina Cornelio, Judy Goldsmith, et al.
JAIR
Salvatore Certo, Anh Pham, et al.
Quantum Machine Intelligence
Els van Herreweghen, Uta Wille
USENIX Workshop on Smartcard Technology 1999
Yi Zhou, Parikshit Ram, et al.
ICLR 2023