演化森林哈希：一种无监督的在线哈希学习算法

寿震宇; 钱江波; 董一鸿; 陈华辉

doi:10.11959/j.issn.1000-0801.2020055

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演化森林哈希：一种无监督的在线哈希学习算法

研究与开发 | 更新时间：2024-06-05

- 演化森林哈希：一种无监督的在线哈希学习算法
- EFH:an online unsupervised hash learning algorithm
- 电信科学 2020年36卷第3期页码：71-82
- 作者机构：
- 作者简介：
  
  [ "寿震宇（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、逻辑电路设计、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" ]
- 基金信息：
  
  浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(LZ20F020001);浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(LY20F020009);国家自然科学基金资助项目;The National Natural Science Foundation of China(61472194);国家自然科学基金资助项目;The National Natural Science Foundation of China(61572266);宁波市自然科学基金资助项目;Ningbo Municipal Natural Science Foundation of China(2019A610085)
- DOI：10.11959/j.issn.1000-0801.2020055
  中图分类号： TP311.3
- 网络出版日期：2020-03，
  
  纸质出版日期：2020-03-20
- 稿件说明：
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寿震宇, 钱江波, 董一鸿, 等. 演化森林哈希：一种无监督的在线哈希学习算法[J]. 电信科学, 2020,36(3):71-82.

Zhenyu SHOU, Jiangbo QIAN, Yihong DONG, et al. EFH:an online unsupervised hash learning algorithm[J]. Telecommunications science, 2020, 36(3): 71-82.
寿震宇, 钱江波, 董一鸿, 等. 演化森林哈希：一种无监督的在线哈希学习算法[J]. 电信科学, 2020,36(3):71-82. DOI： 10.11959/j.issn.1000-0801.2020055.

Zhenyu SHOU, Jiangbo QIAN, Yihong DONG, et al. EFH:an online unsupervised hash learning algorithm[J]. Telecommunications science, 2020, 36(3): 71-82. DOI： 10.11959/j.issn.1000-0801.2020055.

摘要

目前的无监督哈希学习算法在训练阶段需要加载全部的数据，会占据较大的内存空间，并且无法适用于流式数据。探索性地提出了一种无监督在线哈希学习算法——演化森林哈希。针对大规模数据检索场景，通过改进后的演化树学习数据的空间拓扑结构，并提出了路径编码策略将数据点遍历演化树时的路径映射为保相似性二进制编码。为了进一步提高编码查询性能，在演化树哈希的基础上进一步提出在线演化森林哈希，最后在两个被广泛使用的数据集上用实验证明了本文方法的可行性。

Abstract

Many unsupervised learning to hash algorithm needs to load all data to memory in the training phase

which will occupy a large memory space and cannot be applied to streaming data.An unsupervised online learning to hash algorithm called evolutionary forest hash (EFH) was proposed.In a large-scale data retrieval scenario

the improved evolution tree can be used to learn the spatial topology of the data.A path coding strategy was proposed to map leaf nodes to similarity-preserved binary code.To further improve the querying performance

ensemble learning was combined

and an online evolving forest hashing method was proposed based on the evolving trees.Finally

the feasibility of this method was proved by experiments on two widely used data sets.

关键词

Keywords

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