Liya Fan, Fa Zhang, et al.
JPDC
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.
Liya Fan, Fa Zhang, et al.
JPDC
Sashi Novitasari, Takashi Fukuda, et al.
INTERSPEECH 2025
Kellen Cheng, Anna Lisa Gentile, et al.
EMNLP 2024
David Carmel, Haggai Roitman, et al.
ACM TIST