Fitting Spectral Decay with the $k$-Support Norm

no code implementations • 4 Jan 2016 • Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos

The spectral $k$-support norm enjoys good estimation properties in low rank matrix learning problems, empirically outperforming the trace norm.

Matrix Completion

New Perspectives on $k$-Support and Cluster Norms

no code implementations • 27 Dec 2015 • Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos

We note that the spectral box-norm is essentially equivalent to the cluster norm, a multitask learning regularizer introduced by [Jacob et al. 2009a], and which in turn can be interpreted as a perturbation of the spectral k-support norm.

Matrix Completion

Spectral k-Support Norm Regularization

no code implementations • NeurIPS 2014 • Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos

The $k$-support norm has successfully been applied to sparse vector prediction problems.

Matrix Completion

New Perspectives on k-Support and Cluster Norms

no code implementations • 6 Mar 2014 • Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos

We further extend the $k$-support norm to matrices, and we observe that it is a special case of the matrix cluster norm.

Matrix Completion