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.
Graph spectral image processing,
2 Pith papers cite this work. Polarity classification is still indexing.
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Unrolls graph-based Douglas-Rachford iterations with informed initialization from a known interpolator to achieve state-of-the-art image interpolation using fewer network parameters.
citing papers explorer
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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.
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Unrolling Graph-based Douglas-Rachford Algorithm for Image Interpolation with Informed Initialization
Unrolls graph-based Douglas-Rachford iterations with informed initialization from a known interpolator to achieve state-of-the-art image interpolation using fewer network parameters.