Binary Representation via Jointly Personalized Sparse Hashing. (arXiv:2208.14883v1 [cs.CV])

Unsupervised hashing has attracted much attention for binary representation
learning due to the requirement of economical storage and efficiency of binary
codes. It aims to encode high-dimensional features in the Hamming space with
similarity preservation between instances. However, most existing methods learn
hash functions in manifold-based approaches. Those methods capture the local
geometric structures (i.e., pairwise relationships) of data, and lack
satisfactory performance in dealing with real-world scenarios that produce
similar features (e.g. color and shape) with different semantic information. …

arxiv binary hashing personalized representation