Subspace Learning

author: Alessandro Rudi, Istituto Italiano di Tecnologia
published: Aug. 26, 2013,   recorded: July 2013,   views: 5462


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This work deals with the problem of linear subspace estimation in a general, Hilbert space setting. We provide bounds that are considerably sharper than existing ones, under equal assumptions. These bounds are also competitive with bounds that are allowed to make strong, further assumptions (on the fourth order moments), even when we do not. Finally, we generalize these results to a family of metrics, allowing for a more general defi nition of performance.

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