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research-article

Regularized semi-supervised KLFDA algorithm based on density peak clustering

Authors:

Shan HuangAuthors Info & Claims

Neural Computing and Applications, Volume 34, Issue 22

Pages 19791 - 19817

https://doi.org/10.1007/s00521-022-07495-9

Published: 01 November 2022 Publication History

Abstract

To solve the problem that the existing semi-supervised FISHER discriminant analysis algorithm (FDA) cannot effectively use both labeled and unlabeled data for learning, we propose a semi-supervised Kernel local FDA Algorithm based on density peak clustering pseudo-labels (SDPCKLFDA). First, the proposed algorithm adopts the density peak clustering algorithm to generate the pseudo cluster labels for labeled and unlabeled data, and then the generated pseudo-labels are explored to construct two regularization strategies. The two regularization strategies are used to regularize the corresponding within-class scatter matrix and between-class scatter matrix of the local Fisher discriminant analysis, and finally the optimal projection vector is obtained by solving the objective function of the local Fisher discriminant analysis. The two constructed regularization strategies can not only effectively enhance the discriminant performance of the extracted feature but also make the proposed algorithm suitable for multimodal and noisy data. In addition, to accommodate nonlinear and non-Gaussian datasets, we also develop a kernel version of the proposed algorithm with the help of kernel trick. In the experiment, the proposed algorithm is compared with the FDA and its improved algorithms on some benchmark artificial datasets and UCI datasets. The experimental results show that the discriminant performance of the proposed algorithm has been significantly improved compared with the other algorithms.

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Cited By

Xie XSun F(2023)Laplacian generalized elastic net Lp-norm nonparallel support vector machine for semi-supervised classificationNeural Computing and Applications10.1007/s00521-023-08548-335:21(15857-15875)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s00521-023-08548-3

Index Terms

Regularized semi-supervised KLFDA algorithm based on density peak clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
      2. Unsupervised learning
        Cluster analysis
    2. Machine learning approaches

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

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Information & Contributors

Information

Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 34, Issue 22

Nov 2022

1022 pages

ISSN:0941-0643

EISSN:1433-3058

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 November 2022

Accepted: 31 May 2022

Received: 09 September 2021

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Funding Sources

National Natural Science Foundation of China
the Fundamental Research Funds for the Central Universities
Innovative Talent Fund of Harbin Science and Technology Bureau

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Cited By

Xie XSun F(2023)Laplacian generalized elastic net Lp-norm nonparallel support vector machine for semi-supervised classificationNeural Computing and Applications10.1007/s00521-023-08548-335:21(15857-15875)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1007/s00521-023-08548-3

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