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Subspace Learning
This video was recorded at International Workshop on Advances in Regularization, Optimization, Kernel Methods and Support Vector Machines (ROKS): theory and applications, Leuven 2013. 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 definition of performance.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
