Learning Higher-Order Graph Structure with Features by Structure Penalty

NeurIPS 2011 Shilin DingGrace WahbaJerry Zhu

In discrete undirected graphical models, the conditional independence of node labels Y is specified by the graph structure. We study the case where there is another input random vector X (e.g. observed features) such that the distribution P (Y | X) is determined by functions of X that characterize the (higher-order) interactions among the Y ’s... (read more)

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