منابع مشابه
Breaking the curse of dimensionality in regression
Models with many signals, high-dimensional models, often impose structures on the signal strengths. The common assumption is that only a few signals are strong and most of the signals are zero or close (collectively) to zero. However, such a requirement might not be valid in many real-life applications. In this article, we are interested in conducting large-scale inference in models that might ...
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We consider neural networks with a single hidden layer and non-decreasing positively homogeneous activation functions like the rectified linear units. By letting the number of hidden units grow unbounded and using classical non-Euclidean regularization tools on the output weights, they lead to a convex optimization problem and we provide a detailed theoretical analysis of their generalization p...
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The curse of dimensionality has remained a challenge for a wide variety of algorithms in data mining, clustering, classification and privacy. Recently, it was shown that an increasing dimensionality makes the data resistant to effective privacy. The theoretical results seem to suggest that the dimensionality curse is a fundamental barrier to privacy preservation. However, in practice, we show t...
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We study the behavior of pivot-based algorithms for similarity searching in metric spaces. We show that they are eeective tools for intrinsically high-dimensional spaces, and that their performance is basically dependent on the number of pivots used and the precision used to store the distances. In this paper we give a simple yet eeective recipe for practitioners seeking for a black-box method ...
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ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2000
ISSN: 1556-5068
DOI: 10.2139/ssrn.236812