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Minimum rank problems arise frequently in machine learning applications and are notoriously difficult to solve due to the non-convex nature of the rank objective. In this paper, we present the first online learning approach for the problem of rank minimization of matrices over polyhedral sets. In particular, we present two online learning algorithms for rank minimization -our first algorithm is a multiplicative update method based on a generalized experts framework, while our second algorithmdoi:10.1145/1390156.1390239 dblp:conf/icml/MekaJCD08 fatcat:milb7nakhjeunnc7autxx7b33u