Sparse Recovery Problems in High Dimensions: Statistical Inference and Learning Theory

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Date
2009
Authors
Volume
16
Issue
Journal
Series Titel
Oberwolfach reports : OWR
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Publisher
Zürich : EMS Publ. House
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Abstract

The statistical analysis of high dimensional data requires new techniques, extending results from nonparametric statistics, analysis, probability, approximation theory, and theoretical computer science. The main problem is how to unveil, (or to mimic performance of) sparse models for the data. Sparsity is generally meant in terms of the number of variables included, but may also be described in terms of smoothness, entropy, or geometric structures. A key objective is to adapt to unknown sparsity, yet keeping computational feasibility.

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