caa a22 4500 605478333 CHVBK 20210128100405.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s10994-015-5496-x doi (NATIONALLICENCE)springer-10.1007/s10994-015-5496-x Consensus hashing [Elektronische Daten] [Cong Leng, Jian Cheng] Hashing techniques have been widely used in many machine learning applications because of their efficiency in both computation and storage. Although a variety of hashing methods have been proposed, most of them make some implicit assumptions about the statistical or geometrical structure of data. In fact, few hashing algorithms can adequately handle all kinds of data with different structures. When considering hybrid structure datasets, different hashing algorithms might produce different and possibly inconsistent binary codes. Inspired by the successes of classifier combination and clustering ensembles, in this paper, we present a novel combination strategy for multiple hashing results, named consensus hashing. By defining the measure of consensus of two hashing results, we put forward a simple yet effective model to learn consensus hash functions which generate binary codes consistent with the existing ones. Extensive experiments on several large scale benchmarks demonstrate the overall superiority of the proposed method compared with state-of-the-art hashing algorithms. The Author(s), 2015 Leng Cong National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China aut Cheng Jian National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China aut Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 379-398 0885-6125 100:2-3<379 2015 100 10994 https://doi.org/10.1007/s10994-015-5496-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-015-5496-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Leng Cong National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China aut NATIONALLICENCE 700 1- Cheng Jian National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 379-398 0885-6125 100:2-3<379 2015 100 10994