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.
Step 1 to 3 is a variant of the Prim’s algorithm for MST [15], which is more efficient than the Kruskal’s algorithm when the candidate edges (i.e., entries inY) are dense
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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.