Li Xinqi, Wang Jun, Kwong Sam
IEEE Trans Neural Netw Learn Syst. 2022 Oct;33(10):5116-5124. doi: 10.1109/TNNLS.2021.3068500. Epub 2022 Oct 5.
Hash bit selection (HBS) aims to find the most discriminative and informative hash bits from a hash pool generated by using different hashing algorithms. It is usually formulated as a binary quadratic programming problem with an information-theoretic objective function and a string-length constraint. In this article, it is equivalently reformulated in the form of a quadratic unconstrained binary optimization problem by augmenting the objective function with a penalty function. The reformulated problem is solved via collaborative neurodynamic optimization (CNO) with a population of classic discrete Hopfield networks. The two most important hyperparameters of the CNO approach are determined based on Monte Carlo test results. Experimental results on three benchmark data sets are elaborated to substantiate the superiority of the collaborative neurodynamic approach to several existing methods for HBS.
哈希位选择（HBS）旨在从使用不同哈希算法生成的哈希池中找到最具区分性和信息量的哈希位。它通常被表述为一个具有信息论目标函数和字符串长度约束的二元二次规划问题。在本文中，通过用惩罚函数增强目标函数，将其等效地重新表述为二次无约束二元优化问题的形式。通过一群经典离散霍普菲尔德网络的协作神经动力学优化（CNO）来解决重新表述后的问题。基于蒙特卡罗测试结果确定了CNO方法的两个最重要的超参数。阐述了在三个基准数据集上的实验结果，以证实协作神经动力学方法相对于几种现有的HBS方法的优越性。
