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research-article

Unified feature extraction framework based on contrastive learning

Authors:

Ling JingAuthors Info & Claims

Volume 258, Issue C

https://doi.org/10.1016/j.knosys.2022.110028

Published: 22 December 2022 Publication History

Abstract

Feature extraction is an efficient approach for alleviating the issue of dimensionality in high-dimensional data. Contrastive learning (CL), which is a popular self-supervised learning method, has recently attracted considerable attention. In this study, based on a new perspective of CL, we propose a unified framework that is suitable for both unsupervised and supervised feature extraction. In the framework, two CL graphs are first constructed to define the positive and negative pairs uniquely. Subsequently, the projection matrix is determined by minimizing the contrastive loss function. Moreover, the proposed framework considers positive and negative pairs to unify the unsupervised and supervised feature extraction. We propose three specific methods under this framework: unsupervised CL, supervised CL without local preservation, and supervised CL with local preservation. Finally, numerical experiments on six real datasets demonstrate the superior performance of the proposed framework compared to existing methods.

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Highlights

•

A unified feature extraction framework based on contrastive learning is proposed.

•

The framework proposes a novel approach to define positive and negative pairs.

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The framework is regarded as maximizing the mutual information of positive pairs.

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Cited By

Wang QLiu MFeng WJiang MXu HGao QLarson K(2024)Reconstruction weighting principal component analysis with fusion contrastive learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/563(5091-5098)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/563

Index Terms

Unified feature extraction framework based on contrastive learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning algorithms
      1. Feature selection
2. Mathematics of computing

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 258, Issue C

Dec 2022

1196 pages

ISSN:0950-7051

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Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 22 December 2022

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Cited By

Wang QLiu MFeng WJiang MXu HGao QLarson K(2024)Reconstruction weighting principal component analysis with fusion contrastive learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/563(5091-5098)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/563

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