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From statistical estimation to well localized frames, via heat kernel

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If you have a question about this talk, please contact Richard Samworth.

During the last twenty years, wavelet theory has proved to be a very useful tool for theoretical purposes as well as for applications. One of the main reasons is that they provide a sparse representation of signals. In this talk, we will revisit some statistical results due to sparse representations and provide an extension of this theory in a general geometric framework.

This is joint work with T. Coulhon and P.Petrushev.

This talk is part of the Statistics series.

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