Learning Sparse Binary Code for Maximum Inner Product Search

Changyi Ma, Fangchen Yu, Yueyao Yu, Wenye Li

Abstract: Maximum inner product search (MIPS), combined with the hashing method, has become a standard solution to similarity search problems. It often achieves an order of magnitude speedup over nearest neighbor search (NNS) under similar settings. Motivated by the work and achievements along this line, in this paper, we developed a sparse binary hashing method for MIPS to preserve the pairwise similarities with the support of two asymmetric hash functions. We proposed a simple and efficient algorithm that learns two hash functions for the query database and the search database respectively. We conducted experiments to evaluate the proposed method, relying on image retrieval tasks on four benchmark datasets. The empirical results clearly demonstrated the algorithm's promising potential on practical applications in terms of search accuracy and scalability.