Learning a Distance Metric from a Network

NeurIPS 2011 Blake ShawBert HuangTony Jebara

Many real-world networks are described by both connectivity information and features for every node. To better model and understand these networks, we present structure preserving metric learning (SPML), an algorithm for learning a Mahalanobis distance metric from a network such that the learned distances are tied to the inherent connectivity structure of the network... (read more)

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