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Sharp generalization error bounds for randomly-projected classifiers

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 .
Type
Conference Contribution
Type of thesis
Series
Citation
Durrant, R. J., & Kaban, A. (2013). Sharp generalization error bounds for randomly-projected classifiers. In S. Dasgupta & D. McAllester (Eds.), Proceedings of the Thirtieth International Conference on Machine Learning, Atlanta, USA(Vol. JMLR Workshop and Conference Proceedings, Volume 28, p. 693).
Date
2013
Publisher
JMLR
Degree
Supervisors
Rights
This is an author’s accepted version of a paper published in the Proceedings of The 30th International Conference on Machine Learning. © 2013 The Authors.