A convex formulation for learning scale-free networks via submodular relaxation

Defazio, Aaron; Caetano, Tiberio

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A convex formulation for learning scale-free networks via submodular relaxation

Date

2012

Authors

Defazio, Aaron

Caetano, Tiberio

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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http://hdl.handle.net/1885/82731

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Open Access Research

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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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2037-12-31

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A convex formulation for learning scale-free networks via submodular relaxation

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