|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
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.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsConnecting Structure to Function: from Calculation to experiment and back again Unilever Centre for Molecular Science Informatics Category Theory Seminar
Other talksThe strangeness of Andromeda II Tunnelling Under Cities, Advances in Research and Practice Inferno XV, Purgatorio XV, Paradiso XV Cambridge Public Policy Seminar: Title TBC Modeling Tumorigenesis and Drug Resistance in Three Dimensions Rethinking the European Union