Substructure correlation adaptation transfer learning method based on K-means clustering

Haoshuang LIU; Yong ZHANG; Yingbo CAO

doi:10.11959/j.issn.1000-0801.2023045

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Substructure correlation adaptation transfer learning method based on K-means clustering

Research and Development | 更新时间：2024-06-05

- Substructure correlation adaptation transfer learning method based on K-means clustering
- Telecommunications Science Vol. 39, Issue 3, Pages: 124-134(2023)
- 作者机构：
  
  1. 辽宁师范大学计算机与信息技术学院，辽宁大连 116081
  2. 湖州师范学院信息工程学院，浙江湖州 313000
  3. 浙江省现代农业资源智慧管理与应用研究重点实验室，浙江湖州 313000
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023045
  CLC：
- Published Online：2023-03，
  
  Published：20 March 2023
- 稿件说明：
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Haoshuang LIU, Yong ZHANG, Yingbo CAO. Substructure correlation adaptation transfer learning method based on K-means clustering[J]. Telecommunications science, 2023, 39(3): 124-134.
DOI：

Haoshuang LIU, Yong ZHANG, Yingbo CAO. Substructure correlation adaptation transfer learning method based on K-means clustering[J]. Telecommunications science, 2023, 39(3): 124-134. DOI： 10.11959/j.issn.1000-0801.2023045.

摘要

域漂移严重影响了传统机器学习方法的性能，现有的领域自适应方法主要通过全局、类级或样本级分布匹配自适应地调整跨域表示。但全局匹配和类级匹配过于粗糙会导致自适应不足，而样本级匹配受到噪声的影响可能导致过度自适应。基于此，提出了一种基于K均值（K-means）聚类的子结构相关适配（SCOAD）迁移学习算法，首先通过 K-means 聚类分别获得源域和目标域的多个子域，其次寻求子域中心二阶统计量的匹配，最后利用子域内结构对目标域样本进行分类。该方法在传统方法的基础上进一步提高了源域与目标域之间知识迁移的性能。在常用迁移学习数据集上的实验结果表明了所提方法的有效性。

Abstract

Domain drifts severely affect the performance of traditional machine learning methods

and existing domain adaptive methods are mainly represented by adaptive adjustment cross-domain through global

class-level

or sample-level distribution adaptation.However

too coarse global matching and class-level matching can lead to insufficient adaptation

and sample-level adaptation to noise can lead to excessive adaptation.A substructure correlation adaptation (SCOAD) transfer learning algorithm based on K-means clustering was proposed.Firstly

multiple subdomains of the source domain and the target domain were obtained by K-means clustering.Then

the matching of the second-order statistics of the subdomain center was sought.Finally

the target domain samples were classified by using the subdomain structure.The proposed method approach further improves the performance of knowledge transfer between the source and target domains on top of the traditional approach.Experimental results on common transfer learning datasets show the effectiveness of the proposed method.

关键词

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references

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