Fiedler number maximization as regularization, combined with greedy edge selection and Cheeger-cut partitioning, produces more robust sparse connected graph estimates from limited data than prior methods.
Toward the optimal preconditioned eigensolver: Locally optimal block preconditioned conjugate gradient method,
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Sparse Graph Learning from Sparse Data via Fiedler Number Maximization
Fiedler number maximization as regularization, combined with greedy edge selection and Cheeger-cut partitioning, produces more robust sparse connected graph estimates from limited data than prior methods.