# Sparse Robust Classification via the Kernel Mean

arXiv: https://arxiv.org/abs/1506.01520 · HTML: https://mlsanity.com/paper/1506.01520/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2015.

## Built on by (most cited first, 2 of 2)

- [What killed the Convex Booster ?](https://mlsanity.com/paper/2205.09628/) (2022)
- [A Symmetric Loss Perspective of Reliable Machine Learning](https://mlsanity.com/paper/2101.01366/) (2021)

## Abstract (arXiv)

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.