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.
Recursively call (m1, n1) =Partition(G 1)and(m 2, n2) =Partition(G 1)
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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.