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Sharp generalization error bounds for randomly-projected classifiers
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 āwhat is the effect of random projection on the predicted class labels?ā and we therefore derive the exact probability of ālabel flippingā under Gaussian random projection in order to quantify this effect precisely in our bounds
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