Structured Sparsity through Convex Optimization
Structured Sparsity through Convex Optimization
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Publisher
Hayward: Institute of Mathematical Statistics
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Language
English
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Hayward: Institute of Mathematical Statistics
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Contents
Sparse estimation methods are aimed at using or obtaining parsimonious representations of data or models. While naturally cast as a combinatorial optimization problem, variable or feature selection admits a convex relaxation through the regularization by the ℓ₁-norm. In this paper, we consider situations where we are not only interested in sparsity...
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Full title
Structured Sparsity through Convex Optimization
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TN_cdi_projecteuclid_primary_oai_CULeuclid_euclid_ss_1356098550
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_projecteuclid_primary_oai_CULeuclid_euclid_ss_1356098550
Other Identifiers
ISSN
0883-4237
E-ISSN
2168-8745
DOI
10.1214/12-sts394
