Unsupervised hashing based on the recovery of subspace structures

Highlights：

• Our method adapts the LRR model into a new variant, based on which the learned correlation matrix could be designed into a space-and-time saving formula for data semantics.

• To tackle the discrete graph hashing, we presents a new learning method, i.e., transforms the original optimization problem into three subproblems by means of surrogate variables, and most importantly each subproblem is addressed with a closed-form solution, which makes the whole hashing learning converge within dozens of iterations.

• Experiments on four datasets demonstrate the advantages of our method over several state-of- the-art unsupervised hashing models including two recently proposed unsupervised deep hashing methods.