A Note on k-support Norm Regularized Risk Minimization -implementation -

A Note on k-support Norm Regularized Risk Minimization by Matthew Blaschko. The abstract reads:

The k-support norm has been recently introduced to perform correlated sparsity regularization. Although Argyriou et al. only reported experiments using squared loss, here we apply it to several other commonly used settings resulting in novel machine learning algorithms with interesting and familiar limit cases. Source code for the algorithms described here is available.

The attendant code to repoduce the results is available on GitHub. Thanks Matthew !

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