Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 5116-5124 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Neural Networks and Learning Systems |
| Volume | 33 |
| Issue number | 10 |
| Early online date | 9 Apr 2021 |
| DOIs | |
| Publication status | Published - Oct 2022 |
| Externally published | Yes |
Funding
This work was supported in part by the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China under Grant 2018AAA0100300 and Grant 2018AAA0101301 and in part by the Research Grants Council of the Hong Kong Special Administrative Region of China through General Research Fund under Grant 11208517, Grant 11202318, Grant 11202019, Grant 11209819, and Grant 11203820.
Keywords
- Collaborative neurodynamic optimization (CNO)
- discrete Hopfield network (DHN)
- hash bit selection (HBS)