Search Machine Learning Repository:
**Sharp Generalization Error Bounds for Randomly-projected Classifiers**

**Authors:** *Robert Durrant* and *Ata Kaban*

**Conference:** Proceedings of the 30th International Conference on Machine Learning (ICML-13)

**Year:** 2013

**Pages:** 693-701

**Abstract:** We derive sharp bounds on the generalization error of a generic linear classifier trained by empirical risk minimization on randomly-projected data. We make no restrictive assumptions (such as sparsity or separability) on the data: Instead we use the fact that, in a classification setting, the question of interest is really `what is the effect of random projection on the predicted class labels?' and we therefore derive the exact probability of `label flipping' under Gaussian random projection in order to quantify this effect precisely in our bounds.

[pdf] [BibTeX]

authors venues years

Suggest Changes to this paper.

Brought to you by the WUSTL Machine Learning Group. We have open faculty positions (tenured and tenure-track).