GLDH: Toward more efficient global low-density locality-sensitive hashing for high dimensions

• Published in: Information sciences Vol. 533; pp. 43 - 59
• Main Authors: Li, Yiqi, Xiao, Ruliang, Wei, Xin, Liu, Huakun, Zhang, Shi, Du, Xin
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2020
• Subjects: Global low-density hyperplane; Graph cut method; Minimum information gain; Nearest neighbor search; Random maximum entropy; Minimum information gain; Nearest neighbor search; Random maximum entropy; Graph cut method; Global low-density hyperplane
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2020.04.046

Despite decades of intensive effort, the current solutions for efficiently searching high-dimensional data spaces are not entirely satisfactory. This paper proposes a more efficient global low-density locality sensitive hashing search algorithm (GLDH) based on the minimal cut hyperplane and ensemble learning. The innovation is that a novel global low-density hyperplane candidate set is constructed by the graph cut method, the minimum information gain method and random maximum entropy method are used to greedily select the hyperplane, and the ensemble learning method is used to query the global approximate nearest-neighbors data. This paper proves that the GLDH algorithm produces a low error hyperplane partition. The results of extensive experiments show that the proposed GLDH method performs better than the latest methods when using the same hash coding length for datasets from different fields.