  Recently, hashing techniques have gained importance in large-scale retrieval
tasks because of their retrieval speed. Most of the existing cross-view
frameworks assume that data are well paired. However, the fully-paired
multiview situation is not universal in real applications. The aim of the
method proposed in this paper is to learn the hashing function for semi-paired
cross-view retrieval tasks. To utilize the label information of partial data,
we propose a semi-supervised hashing learning framework which jointly performs
feature extraction and classifier learning. The experimental results on two
datasets show that our method outperforms several state-of-the-art methods in
terms of retrieval accuracy.
