  Hashing method maps similar data to binary hashcodes with smaller hamming
distance, and it has received a broad attention due to its low storage cost and
fast retrieval speed. However, the existing limitations make the present
algorithms difficult to deal with large-scale datasets: (1) discrete
constraints are involved in the learning of the hash function; (2) pairwise or
triplet similarity is adopted to generate efficient hashcodes, resulting both
time and space complexity are greater than O(n^2). To address these issues, we
propose a novel discrete supervised hash learning framework which can be
scalable to large-scale datasets. First, the discrete learning procedure is
decomposed into a binary classifier learning scheme and binary codes learning
scheme, which makes the learning procedure more efficient. Second, we adopt the
Asymmetric Low-rank Matrix Factorization and propose the Fast Clustering-based
Batch Coordinate Descent method, such that the time and space complexity is
reduced to O(n). The proposed framework also provides a flexible paradigm to
incorporate with arbitrary hash function, including deep neural networks and
kernel methods. Experiments on large-scale datasets demonstrate that the
proposed method is superior or comparable with state-of-the-art hashing
algorithms.
