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Collaborative Low-Rank Subspace Clustering

Abstract · Apr 13, 2017 01:41 ·

low unified australia collaborative subspace observations sydney mathematics nsw cs-cv

Arxiv Abstract

  • Stephen Tierney
  • Yi Guo
  • Junbin Gao

In this paper we present Collaborative Low-Rank Subspace Clustering. Given multiple observations of a phenomenon we learn a unified representation matrix. This unified matrix incorporates the features from all the observations, thus increasing the discriminative power compared with learning the representation matrix on each observation separately. Experimental evaluation shows that our method outperforms subspace clustering on separate observations and the state of the art collaborative learning algorithm.

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