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We propose a fast general projection-free metric learning framework, where the minimization objective $\min_{\M \in \cS} Q(\M)$ is a convex differentiable function of the metric matrix $\M$, and $\M$ resides in the set $\cS$ of generalized graph Laplacian
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Graph Metric Learning Via Gershgorin Disc Alignment
We propose a fast general projection-free metric learning framework, where the minimization objective $min_{M in cS} Q(M)$ is a convex differentiable function of the metric matrix $M$, and $M$ resides in the set $cS$ of generalized graph Laplacian