Sparse Robust Classification via the Kernel Mean
Machine Learning
2025-10-14 v4 Machine Learning
Abstract
Many leading classification algorithms output a classifier that is a weighted average of kernel evaluations. Optimizing these weights is a nontrivial problem that still attracts much research effort. Furthermore, explaining these methods to the uninitiated is a difficult task. Letting all the weights be equal leads to a conceptually simpler classification rule, one that requires little effort to motivate or explain, the mean. Here we explore the consistency, robustness and sparsification of this simple classification rule.
Cite
@article{arxiv.1506.01520,
title = {Sparse Robust Classification via the Kernel Mean},
author = {Brendan van Rooyen and Aditya Krishna Menon and Robert C. Williamson},
journal= {arXiv preprint arXiv:1506.01520},
year = {2025}
}