research-article

Discriminative and Geometry-Preserving Adaptive Graph Embedding for dimensionality reduction

Authors:

Yi ZhangAuthors Info & Claims

Volume 157, Issue C

Pages 364 - 376

https://doi.org/10.1016/j.neunet.2022.10.024

Published: 01 January 2023 Publication History

Abstract

Learning graph embeddings for high-dimensional data is an important technology for dimensionality reduction. The learning process is expected to preserve the discriminative and geometric information of high-dimensional data in a new low-dimensional subspace via either manual or automatic graph construction. Although both manual and automatic graph constructions can capture the geometry and discrimination of data to a certain degree, they working alone cannot fully explore the underlying data structure. To learn and preserve more discriminative and geometric information of the high-dimensional data in the low-dimensional subspace as much as possible, we develop a novel Discriminative and Geometry-Preserving Adaptive Graph Embedding (DGPAGE). It systematically integrates manual and adaptive graph constructions in one unified graph embedding framework, which is able to effectively inject the essential information of data involved in predefined graphs into the learning of an adaptive graph, in order to achieve both adaptability and specificity of data. Learning the adaptive graph jointly with the optimized projections, DGPAGE can generate an embedded subspace that has better pattern discrimination for image classification. Results derived from extensive experiments on image data sets have shown that DGPAGE outperforms the state-of-the-art graph-based dimensionality reduction methods. The ablation studies show that it is beneficial to have an integrated framework, like DGPAGE, that brings together the advantages of manual/adaptive graph construction.

Highlights

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Predefined similarity and diversity adjacency graphs.

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Inject the manual graphs into learning automatic graph.

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Design DGPAGE via unifying the learning of projection and adaptive graph.

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Cited By

Tao HCao JChen LSun HShi YZhu X(2024)Black-box attacks on dynamic graphs via adversarial topology perturbationsNeural Networks10.1016/j.neunet.2023.11.060171:C(308-319)Online publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.neunet.2023.11.060
Li BYang ZGuo A(2024)P2S distance induced locally conjugated orthogonal subspace learning for feature extractionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122170238:PEOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122170
Cirac GFarfan JAvansi GSchiozer DRocha A(2023)Cross-Domain Feature learning and data augmentation for few-shot proxy development in oil industryApplied Soft Computing10.1016/j.asoc.2023.110972149:PAOnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110972

Index Terms

Discriminative and Geometry-Preserving Adaptive Graph Embedding for dimensionality reduction
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning

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

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Published In

cover image Neural Networks

Neural Networks Volume 157, Issue C

Jan 2023

514 pages

ISSN:0893-6080

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 January 2023

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Cited By

Tao HCao JChen LSun HShi YZhu X(2024)Black-box attacks on dynamic graphs via adversarial topology perturbationsNeural Networks10.1016/j.neunet.2023.11.060171:C(308-319)Online publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.neunet.2023.11.060
Li BYang ZGuo A(2024)P2S distance induced locally conjugated orthogonal subspace learning for feature extractionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122170238:PEOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122170
Cirac GFarfan JAvansi GSchiozer DRocha A(2023)Cross-Domain Feature learning and data augmentation for few-shot proxy development in oil industryApplied Soft Computing10.1016/j.asoc.2023.110972149:PAOnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110972

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