计算机科学
与K无关的哈希
散列函数
线性哈希
量化(信号处理)
离散优化
哈希表
一致哈希
局部敏感散列
双重哈希
动态完美哈希
理论计算机科学
通用哈希
算法
二进制代码
二进制数
计算机安全
数学
最优化问题
算术
作者
Shengnan Wang,Chunguang Li
出处
期刊:IEEE Transactions on Big Data
[Institute of Electrical and Electronics Engineers]
日期:2019-01-01
卷期号:: 1-1
被引量:8
标识
DOI:10.1109/tbdata.2019.2946616
摘要
Hashing has been widely used for nearest neighbors search over big data. Hashing encodes high dimensional data points into binary codes. Most hashing methods use the single-bit quantization (SBQ) strategy for coding the data. However, this strategy often encodes neighboring points into totally different bits. Recently, a double-bit quantization (DBQ) strategy was proposed, which can better preserve the similarity of the data. The hashing problems are generally NP-hard, due to the discrete constraints. For tractability, some relaxation methods were proposed by discarding the discrete constraints. However, such a manner makes the hash codes less effective, due to the large quantization error. To obtain high-quality hash codes, some discrete hashing methods were proposed, which directly solve the hashing problem without any relaxations. However, the existing discrete hashing methods can only deal with single-bit hashing. In this paper, we propose a discrete hashing method to solve double-bit hashing problems. To address the difficulty brought by the discrete constraints, we propose a method to transform the discrete hashing problem into an equivalent continuous optimization problem. Then, we devise algorithms based on DC (difference of convex functions) programming to solve the problem. Numerical experiments are provided to show the superiority of the proposed methods.
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