Fusion Subspace Clustering for Incomplete Data

  title={Fusion Subspace Clustering for Incomplete Data},
  author={U. Mahmood and Daniel Pimentel-Alarc'on},
—This paper introduces fusion subspace clustering , a novel method to learn low-dimensional structures that ap-proximate large scale yet highly incomplete data. The main idea is to assign each datum to a subspace of its own, and minimize the distance between the subspaces of all data, so that subspaces of the same cluster get fused together. Our method allows low, high, and even full-rank data; it directly accounts for noise, and its sample complexity approaches the information-theoretic limit… 

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