基于K-means聚类的子结构相关适配迁移学习方法

刘昊双; 张永; 曹莹波

doi:10.11959/j.issn.1000-0801.2023045

您当前的位置：

首页 >

文章列表页 >

基于K-means聚类的子结构相关适配迁移学习方法

研究与开发 | 更新时间：2024-06-05

- 基于K-means聚类的子结构相关适配迁移学习方法
- Substructure correlation adaptation transfer learning method based on K-means clustering
- 电信科学 2023年39卷第3期页码：124-134
- 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：
  
  [ "刘昊双（1996- ），女，辽宁师范大学硕士生，主要研究方向为数据挖掘、机器学习" ]
  [ "张永（1975- ），男，博士，湖州师范学院教授、博士生导师，主要研究方向为数据挖掘、智能计算" ]
  [ "曹莹波（1999- ），女，辽宁师范大学硕士生，主要研究方向为机器学习、智能计算" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61772252);辽宁省教育厅科学研究经费资助项目;The Scientific Research Foundation of the Education Department of Liaoning Province(LJKZ0965)
- DOI：10.11959/j.issn.1000-0801.2023045
  中图分类号：
- 网络出版日期：2023-03，
  
  纸质出版日期：2023-03-20
- 稿件说明：
移动端阅览
刘昊双, 张永, 曹莹波. 基于K-means聚类的子结构相关适配迁移学习方法[J]. 电信科学, 2023,39(3):124-134.

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.
刘昊双, 张永, 曹莹波. 基于K-means聚类的子结构相关适配迁移学习方法[J]. 电信科学, 2023,39(3):124-134. DOI： 10.11959/j.issn.1000-0801.2023045.

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

Keywords

references

ZHUANG F Z , QI Z Y , DUAN K Y , et al . A comprehensive survey on transfer learning [J ] . Proceedings of the IEEE , 2021 , 109 ( 1 ): 43 - 76 .

GRETTON A , BORGWARDT K M , RASCH M J , et al . A kernel two-sample test [J ] . The Journal of Machine Learning Research , 2012 , 13 ( 1 ): 723 - 773 .

SUN B C , FENG J S , SAENKO K . Return of frustratingly easy domain adaptation [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2016 , 30 ( 1 ): 2058 - 2065 .

GAO J , HUANG R , LI H X . Sub-domain adaptation learning methodology [J ] . Information Sciences , 2015 ( 298 ): 237 - 256 .

ZHU Y C , ZHUANG F Z , WANG J D , et al . Deep subdomain adaptation network for image classification [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2021 , 32 ( 4 ): 1713 - 1722 .

KRISHNA K , NARASIMHA M M . Genetic K-means algorithm [J ] . IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics) , 1999 , 29 ( 3 ): 433 - 439 .

HARTIGAN J A , WONG M A . A K-means clustering algorithm [J ] . Journal of the Royal Statistical Society Series C:Applied Statistics , 1979 , 28 ( 1 ): 100 - 108 .

LECUN Y , BOTTOU L , BENGIO Y , et al . Gradient-based learning applied to document recognition [J ] . Proceedings of the IEEE , 1998 , 86 ( 11 ): 2278 - 2324 .

HULL J J . A database for handwritten text recognition research [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 1994 , 16 ( 5 ): 550 - 554 .

GONG B Q , SHI Y , SHA F , et al . Geodesic flow kernel for unsupervised domain adaptation [C ] // Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2012 : 2066 - 2073 .

CHEN M , XU Z , WEINBERGER K , et al . Marginalized denoising autoencoders for domain adaptation [EB ] . 2012 :arXiv:1206.4683.

JIANG S Y , XU Y H , SONG H J , et al . Multi-instance transfer metric learning by weighted distribution and consistent maximum likelihood estimation [J ] . Neurocomputing , 2018 ( 321 ): 49 - 60 .

ZUO H , ZHANG G Q , BEHBOOD V , et al . Transfer learning in hierarchical feature spaces [C ] // Proceedings of 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) . Piscataway:IEEE Press , 2016 : 183 - 188 .

ZHAO Z , CHEN Y , LIU J , et al . Cross-people mobile-phone based activity recognition [C ] // Proceedings of the 22th International Joint Conference on Artificial Intelligence . Barcelona,Catalonia,Spain:AAAI Press , 2011 : 2545 - 2550 .

WEI Y , ZHU Y , LEUNG C , et al . Instilling social to physical:Co-regularized heterogeneous transfer learning [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2016 , 30 ( 1 ): 1338 - 1344 .

MIHALKOVA L , HUYNH T , MOONEY R J . Mapping and revising Markov logic networks for transfer learning [C ] // Proceedings of the 22nd National Conference on Artificial intelligence . Vancouver,BC,Canada:AAAI Press , 2007 : 608 - 614 .

MIHALKOVA L , MOONEY R J . Transfer learning by mapping with minimal target data [C ] // Proceedings of the AAAI-08 Workshop on Transfer Learning for Complex Tasks . Chicago,IL,USA:AAAI Press , 2008 : 1163 - 1168 .

WANG M , DENG W . Deep visual domain adaptation:a survey [J ] . Neurocomputing , 2018 ( 312 ): 135 - 153 .

LI J , HE R , YE H , et al . Unsupervised domain adaptation of a pretrained cross-lingual language mode [J ] . ArXiv , 2020 ,preprint arXiv:2011.11499.

DAI Y , LIU J , REN X C , et al . Adversarial training based multi-source unsupervised domain adaptation for sentiment analysis [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2020 , 34 ( 5 ): 7618 - 7625 .

PAN S J , TSANG I W , KWOK J T , et al . Domain adaptation via transfer component analysis [J ] . IEEE Transactions on Neural Networks , 2011 , 22 ( 2 ): 199 - 210 .

LONG M S , WANG J M , DING G G , et al . Transfer feature learning with joint distribution adaptation [C ] // Proceedings of 2013 IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2014 : 2200 - 2207 .

WANG J D , CHEN Y Q , HAO S J , et al . Balanced distribution adaptation for transfer learning [C ] // Proceedings of 2017 IEEE International Conference on Data Mining (ICDM) . Piscataway:IEEE Press , 2017 : 1129 - 1134 .

COURTY N , FLAMARY R , TUIA D , et al . Optimal transport for domain adaptation [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 ( 9 ): 1853 - 1865 .

DAS D , LEE C S G . Unsupervised domain adaptation using regularized hyper-graph matching [C ] // Proceedings of 2018 25th IEEE International Conference on Image Processing (ICIP) . Piscataway:IEEE Press , 2018 : 3758 - 3762 .

PEI Z Y , CAO Z J , LONG M S , et al . Multi-adversarial domain adaptation [C ] // Proceedings of the thirty-second AAAI Conference on Artificial Intelligence . New Orleans,LA,USA:AAAI Press , 2018 .

KUMAR A , SATTIGERI P , WADHAWAN K , et al . Co-regularized alignment for unsupervised domain adaptation- [EB ] . 2018,arXiv,1811.05443 .

ZHAO J C , DENG F , HE H B , et al . Local domain adaptation for cross-domain activity recognition [J ] . IEEE Transactions on Human-Machine Systems , 2021 , 51 ( 1 ): 12 - 21 .

ESTER M , KRIEGEL H P , SANDER J , et al . A density-based algorithm for discovering clusters in large spatial databases with noise [C ] // Proceedings of the Second International Conference on Knowledge Discovery and Data Mining . Portland,OR,USA:AAAI Press , 1996 : 226 - 231 .

徐晓 , 丁世飞 , 孙统风 , 等 . 基于网格筛选的大规模密度峰值聚类算法 [J ] . 计算机研究与发展 , 2018 , 55 ( 11 ): 2419 - 2429 .

XU X , DING S F , SUN T F , et al . Large-scale density peaks clustering algorithm based on grid screening [J ] . Journal of Computer Research and Development , 2018 , 55 ( 11 ): 2419 - 2429 .

LONG M S , WANG J M , DING G G , et al . Transfer joint matching for unsupervised domain adaptation [C ] // Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition . New York:ACM , 2014 : 1410 - 1417 .

WANG J D , CHEN Y Q , YU H , et al . Easy transfer learning by exploiting intra-domain structures [C ] // Proceedings of 2019 IEEE International Conference on Multimedia and Expo (ICME) . Piscataway:IEEE Press , 2019 : 1210 - 1215 .

ZHANG J , LI W Q , OGUNBONA P . Joint geometrical and statistical alignment for visual domain adaptation [C ] // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2017 : 5150 - 5158 .

GONG B Q , GRAUMAN K , SHA F . Reshaping visual datasets for domain adaptation [C ] // Proceedings of the 26th International Conference on Neural Information Processing Systems-Volume 1 . New York:ACM , 2013 : 1286 - 1294 .

浏览量

430

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于特征校正的多对抗域适应方法

基于Patch域对抗训练的语音增强

小样本下雷达复合干扰半监督迁移学习识别方法

基于异构数据的电力短期负荷大数据预测方案

基于知识流和迁移学习的负荷预测