A convex formulation for learning scale-free networks via submodular relaxation
Date
2012
Authors
Defazio, Aaron
Caetano, Tiberio
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Publisher
Neural Information Processing Systems Foundation
Abstract
A key problem in statistics and machine learning is the determination of network structure from data. We consider the case where the structure of the graph to be reconstructed is known to be scale-free. We show that in such cases it is natural to formulat
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Keywords
Keywords: Convex optimization problems; Convex relaxation; Gaussian graphical models; Network structures; Scale free networks; Structured sparsities; Submodular functions; Tractable class; Convex optimization; Relaxation processes
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NEURAL INFORMATION PROCESSING SYSTEMS. Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012
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Conference paper
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Open Access
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