With the rapid development of GPU (Graphics Processing Unit) technologies and
neural networks, we can explore more appropriate data structures and
algorithms. Recent progress shows that neural networks can partly replace
traditional data structures. In this paper, we proposed a novel DNN (Deep
Neural Network)-based learned locality-sensitive hashing, called LLSH, to
efficiently and flexibly map high-dimensional data to low-dimensional space.
LLSH replaces the traditional LSH (Locality-sensitive Hashing) function
families with parallel multi-layer neural networks, which reduces the time and
memory consumption and guarantees query accuracy simultaneously. The proposed
LLSH demonstrate the feasibility of replacing the hash index with
learning-based neural networks and open a new door for developers to design and
configure data organization more accurately to improve information-searching
performance. Extensive experiments on different types of datasets show the
superiority of the proposed method in query accuracy, time consumption, and
memory usage.

利用深度神经网络 (LLSH) 代替传统的局部敏感哈希函数族，该方法能够高效灵活地将高维数据映射到低维空间，并在同时减少时间和内存消耗、保证查询准确性方面展示了可行性，为开发人员设计和配置数据组织提供了新思路，以提高信息搜索性能。通过在不同类型数据集上进行广泛实验，验证了该方法在查询准确性、时间消耗和内存使用方面的优越性。