电信科学 ›› 2018, Vol. 34 ›› Issue (10): 104-115.doi: 10.11959/j.issn.1000-0801.2018274
修回日期:
2018-10-10
出版日期:
2018-10-01
发布日期:
2018-11-08
作者简介:
袁明汶 (1993-),男,宁波大学信息科学与工程学院硕士生,主要研究方向为机器学习、人工智能、大数据检索。|钱江波(1974-),男,博士,宁波大学信息科学与工程学院教授,主要研究方向为数据处理与挖掘、机器学习、多维索引与查询优化。|董一鸿(1969-),男,博士,宁波大学信息科学与工程学院教授,主要研究方向为大数据、数据挖掘和人工智能。|陈华辉(1964-),男,博士,宁波大学信息科学与工程学院教授,主要研究方向为数据处理与挖掘、云计算。
基金资助:
Mingwen YUAN,Jiangbo QIAN(),Yihong DONG,Huahui CHEN
Revised:
2018-10-10
Online:
2018-10-01
Published:
2018-11-08
Supported by:
摘要:
大数据时代,数据呈现维度高、数据量大和增长快等特点。面对大量的复杂数据,如何高效地检索相似近邻数据是近似最近邻查询的研究热点。散列技术通过将数据映射为二进制码的方式,能够显著加快相似性计算,并在检索过程中节省存储和通信开销。近年来深度学习在提取数据特征方面表现出速度快、精度高等优异的性能,使得基于深度学习的散列检索技术得到越来越广泛的运用。总结了深度学习散列的主要方法和前沿进展,并对未来的研究方向展开简要探讨。
中图分类号:
袁明汶,钱江波,董一鸿,陈华辉. 基于深度学习的散列检索技术研究进展[J]. 电信科学, 2018, 34(10): 104-115.
Mingwen YUAN,Jiangbo QIAN,Yihong DONG,Huahui CHEN. Research and development of hash retrieval technology based on deep learning[J]. Telecommunications Science, 2018, 34(10): 104-115.
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