WQ:基于权重求解的哈希算法

由于最近邻查询算法一般需要较高时间和空间代价，往往不能满足大数据查询的需要.哈希技术可以大幅度减少查询时间和存储空间，其主要思想是将原始空间中的高维数据映射成为一组编码，且满足保相似性原则.现有的大部分哈希方法一般认为哈希编码的各维度权重相同.然而在实际情况中，不同的维度往往携带有不同的信息.为此，本文提出了新的算法，为编码的每个维度分配权重，并提出了对应的量化编码方式.理论证明了算法的可行性，在真实数据集下与其他哈希算法对比实验也验证了该算法的有效性.

WQ:Hashing Algorithm Based on Bits Weights

Many nearest neighbor query algorithms often fail to meet the query requirements on big data due to their high time and space cost.Hash query technology can significantly reduce not only query time, but also required storage cost.The main principle is to map the high-dimensional data into a set of binary codes with locality preserved.However, most existing hashing methods do not consider the weight differences between the binary bits when calculating the Hamming distances between those binary codes from data.Generally, different hashing bits may contain different amount of information.Focusing on the above issue, this paper proposes WQ (Weighted Quantization) that will assign different weights for each bit of the binary code, as well as a corresponding quantization method.Experimental results show that WQ algorithm has superior performance of data retrieval compared with several other hashing methods.