Logdet rank minimization with application to subspace clustering

HIGHLIGHTS

• who: Subspace Clustering and collaborators from the Computer Science Department, Southern Illinois University, Carbondale, IL, USA have published the Article: LogDet Rank Minimization with Application to Subspace Clustering, in the Journal: (JOURNAL) of 25/03/2015
• what: The authors propose using a log-determinant (LogDet) function as a smooth and closer though nonconvex approximation to rank for obtaining a low-rank representation in Augmented Lagrange multipliers strategy is applied to iteratively optimize the LogDet-based nonconvex objective function on potentially large-scale data. Different from the nuclear norm-based approaches which minimize the summation of . . .
  

   

  

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