rapid-communication

Weighted multi-view common subspace learning method

Authors:

Zhihui LiAuthors Info & Claims

Volume 151, Issue C

Pages 355 - 361

https://doi.org/10.1016/j.patrec.2021.09.017

Published: 01 November 2021 Publication History

Highlights

•

A novel multi-view common subspace learning model is proposed.

•

The model can obtain a unified common subspace by using the discriminant information within and between views.

•

The discriminant information within and between views can be adjusted by the weighted coefficient.

•

Our model adopts the maximize scatter difference criterion as the metric between and within the views after projection.

Abstract

How to use multi-view data effectively has become one of the challenging problems in the computer vision community. The existing multi-view learning methods are mainly based on common subspace learning which aim to explore the discriminative information between multi-view data and find its potential common subspace. Most of the existing multi-view subspace learning methods rely on the within-class scatter matrix and between-class scatter matrix while capturing the discriminative information of multiple views. However, these methods just roughly minimize the within-class distance and maximize the between-class distance, and do not make full use of the intra-view and inter-view information. To address this problem, we propose a weighted common subspace learning method, which can effectively adjust the contribution ratio of between-class and within-class information through a weighted parameter, so that an optimized common subspace can be obtained. And we use the maximum scatter difference criterion as the metric of inter-view and intra-view after projection. Extensive experiments on the public data sets show the superiority of this method.

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Cited By

Gao MHuang W(2024)Latent space search approach for domain adaptationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123770249:PCOnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123770
Shi MZhao XYin XChang XNiu FGuo J(2023)Multiview Latent Structure LearningKnowledge-Based Systems10.1016/j.knosys.2023.110707276:COnline publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110707
Shi MLi ZZhao XXu PLiu BGuo J(2022)Trace ratio criterion for multi-view discriminant analysisApplied Intelligence10.1007/s10489-022-03464-w52:13(14679-14692)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10489-022-03464-w

Index Terms

Weighted multi-view common subspace learning method
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Machine learning
    1. Learning paradigms

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

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Published In

cover image Pattern Recognition Letters

Pattern Recognition Letters Volume 151, Issue C

Nov 2021

362 pages

ISSN:0167-8655

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Copyright © 2021.

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Elsevier Science Inc.

United States

Publication History

Published: 01 November 2021

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Cited By

Gao MHuang W(2024)Latent space search approach for domain adaptationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123770249:PCOnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123770
Shi MZhao XYin XChang XNiu FGuo J(2023)Multiview Latent Structure LearningKnowledge-Based Systems10.1016/j.knosys.2023.110707276:COnline publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110707
Shi MLi ZZhao XXu PLiu BGuo J(2022)Trace ratio criterion for multi-view discriminant analysisApplied Intelligence10.1007/s10489-022-03464-w52:13(14679-14692)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10489-022-03464-w

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