Robust multilayer bootstrap networks in ensemble for unsupervised representation learning and clustering

被引:0
|
作者
Zhang, Xiao-Lei [1 ,2 ,3 ]
Li, Xuelong [2 ]
机构
[1] Northwestern Polytech Univ, Sch Marine Sci & Technol, Xian 710072, Shaanxi, Peoples R China
[2] China Telecom, Inst Artificial Intelligence TeleAI, Beijing 710072, Peoples R China
[3] Northwestern Polytech Univ, Res & Dev Inst, Shenzhen, Peoples R China
基金
美国国家科学基金会;
关键词
Ensemble selection; Cluster ensemble; Multilayer bootstrap networks; Unsupervised learning;
D O I
10.1016/j.patcog.2024.110739
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
It is known that unsupervised nonlinear learning is sensitive to the selection of hyperparameters, which hinders its practical use. How to determine the optimal hyperparameter setting that may be dramatically different across applications is a hard issue. In this paper, we aim to address this issue for multilayer bootstrap networks (MBN), a recent unsupervised model, in a way as simple as possible. Specifically, we first propose an MBN ensemble (MBN-E) algorithm which concatenates the sparse outputs of a set of MBN base models with different network structures into a new representation. Then, we take the new representation produced by MBN-E as a reference for selecting the optimal MBN base models. Moreover, we propose a fast version of MBN-E (fMBN-E), which is not only theoretically even faster than a single standard MBN but also does not increase the estimation error of MBN-E. Empirically, comparing to a number of advanced clustering methods, the proposed methods reach reasonable performance in their default settings. fMBN-E is empirically hundreds of times faster than MBN-E without suffering performance degradation. The applications to image segmentation and graph data mining further demonstrate the advantage of the proposed methods.
引用
收藏
页数:14
相关论文
共 50 条
  • [31] Ensemble Clustering With Attentional Representation
    Hao, Zhezheng
    Lu, Zhoumin
    Li, Guoxu
    Nie, Feiping
    Wang, Rong
    Li, Xuelong
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2024, 36 (02) : 581 - 593
  • [32] Robust representation learning for heterogeneous attributed networks
    Fu, Yue
    Yu, Xinyi
    Wu, Yongliang
    Ding, Xueyi
    Zhao, Shuliang
    INFORMATION SCIENCES, 2023, 628 : 22 - 49
  • [33] AutoCluster: Meta-learning Based Ensemble Method for Automated Unsupervised Clustering
    Liu, Yue
    Li, Shuang
    Tian, Wenjie
    ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2021, PT III, 2021, 12714 : 246 - 258
  • [34] Robust Graph Neural Networks via Ensemble Learning
    Lin, Qi
    Yu, Shuo
    Sun, Ke
    Zhao, Wenhong
    Alfarraj, Osama
    Tolba, Amr
    Xia, Feng
    MATHEMATICS, 2022, 10 (08)
  • [35] Analysis of Unsupervised Machine Learning Techniques for an Efficient Customer Segmentation using Clustering Ensemble and Spectral Clustering
    Hicham, Nouri
    Karim, Sabri
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2022, 13 (10) : 122 - 130
  • [36] Robust Spectral Ensemble Clustering
    Tao, Zhiqiang
    Liu, Hongfu
    Li, Sheng
    Fu, Yun
    CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 2016, : 367 - 376
  • [37] Improving unsupervised pedestrian re-identification with enhanced feature representation and robust clustering
    Luo, Jiang
    Liu, Lingjun
    IET COMPUTER VISION, 2024, 18 (08) : 1097 - 1111
  • [38] Sampling strategies in Siamese Networks for unsupervised speech representation learning
    Riad, Rachid
    Dancette, Corentin
    Karadayi, Julien
    Zeghidour, Neil
    Schatz, Thomas
    Dupoux, Emmanuel
    19TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2018), VOLS 1-6: SPEECH RESEARCH FOR EMERGING MARKETS IN MULTILINGUAL SOCIETIES, 2018, : 2658 - 2662
  • [39] Robust Federated Learning Based on Metrics Learning and Unsupervised Clustering for Malicious Data Detection
    Li, Jiaming
    Zhang, Xinyue
    Zhao, Liang
    ACMSE 2022: PROCEEDINGS OF THE 2022 ACM SOUTHEAST CONFERENCE, 2022, : 238 - 242
  • [40] Balanced bootstrap based ensemble learning
    Zhu, Xiaofei
    Chen, Long
    Proceedings of 2006 International Conference on Artificial Intelligence: 50 YEARS' ACHIEVEMENTS, FUTURE DIRECTIONS AND SOCIAL IMPACTS, 2006, : 660 - 663