Scalable Discrete Supervised Hash Learning with Asymmetric Matrix Factorization
Scalable Discrete Supervised Hash Learning with Asymmetric Matrix Factorization
Hashing methods map 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 …