Signed Graph Metric Learning via Gershgorin Disc Perfect Alignment.

  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},
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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Graph Metric Learning via Gershgorin Disc Alignment
  • Cheng Yang, Gene Cheung, Wei Hu
  • Computer Science
    ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2020
It is proved that left-ends of the Gershgorin discs can be aligned perfectly using the first eigenvector v of M, which is updated iteratively using Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) with warm start as diagonal / off-diagonal terms are optimized.
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