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Grouping strategies for denoising
If you have a question about this talk, please contact Richard Samworth.
We investigate the statistical learning approach for modeling various applications. This modeling involves several phases which need to be solved : the first one often is an approximation step, where we need to translate the observations into a dictionary. The choice of this dictionary (wavelets, needlets, variouslets,..., combinations of several bases,...) often conceals a significant part of investigation. The second phase is the treatment of very high dimensional data (ultra-high dimension means that the number of parameters may grow exponentially faster than the number of observations). This phase is requiring optimization methods of different style : $l_1$ minimizers, multi steps methods,..., as well as concentration inequalities. We concentrate on two steps thresholding methods and observe that making groups in the coefficients can seriously improve the selection and prediction rates. We provide a ‘boosting-grouping’ strategy, taking into account this observation.
This talk is part of the Statistics series.
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Other listsDevelopmental Biology Seminar Series Machine Intelligence Lab Seminar Type the title of a new list here
Other talksThe 5th Annual Sir John Walker Lecture, "The molecular calcium reporter: molecular identity and physiological role" Production Processes Group Seminar - TBC Venture Capital: Supporting Technological Innovation Kirsten Bomblies (John Innes Centre)- Title to be confirmed. Multidimensional IRT models and their applications to psychological testing TBC