# Signed Graph Metric Learning via Gershgorin Disc Perfect Alignment.

@article{Yang2021SignedGM, title={Signed Graph Metric Learning via Gershgorin Disc Perfect Alignment.}, author={Cheng Yang and Gene Cheung and Wei Hu}, journal={IEEE transactions on pattern analysis and machine intelligence}, year={2021}, volume={PP} }

Given a convex and differentiable objective [Formula: see text] for a real symmetric matrix [Formula: see text] in the positive definite (PD) cone, we propose a fast general metric learning framework that is entirely projection-free. We first assume that [Formula: see text] resides in a space [Formula: see text] of generalized graph Laplacian matrices corresponding to balanced signed graphs. Unlike low-rank metric matrices common in the literature, [Formula: see text] includes the important…

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