Adaptive Hashing With Sparse Modification

By representing data through compact binary encodings, hashing techniques have been widely used for data retrieval because of their large data storage and efficient computational time. Most hashing algorithms typically learn a finite number of data projections, but learning optimal projections remains unaddressed. To deal with this limitation, a novel approach, dubbed Adaptive Hashing with Sparse Modification(AHSM), is proposed that learns binary indices composed of the vertices of hypercube and the projection matrix are comprised of two matrices in this paper. The first matrix is orthogonal for rotating data and the second one is sparse for modifying data. The essence of our scheme is that an optimal transformation of data is learned to minimize quantization distortion and improve model fidelity. AHSM has two contributions: the first one is accuracy improvement, and the second one is no increase on computational complexity. Through experiments, we find that AHSM substantially surpasses several state-of-the-art hashing schemes on three data sets.