research-article

Distance-Preserving Embedding Adaptive Bipartite Graph Multi-View Learning with Application to Multi-Label Classification

Authors:

Congyan LangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 2

Article No.: 19, Pages 1 - 21

https://doi.org/10.1145/3537900

Published: 20 February 2023 Publication History

Abstract

Graph-based multi-view learning has attracted much attention due to the efficacy of fusing the information from different views. However, most of them exhibit high computational complexity. We propose an anchor-based bipartite graph embedding approach to accelerate the learning process. Specifically, different from existing anchor-based methods where anchors are obtained from key samples by clustering or weighted averaging strategies, in this article, the anchors are learned in a principled fashion which aims at constructing a distance-preserving embedding for each view from samples to their representations, whose elements are the weights of the edges linking corresponding samples and anchors. In addition, the consistency among different views can be explored by imposing a low-rank constraint on the concatenated embedding representations. We further design a concise yet effective feature collinearity guided feature selection scheme to learn tight multi-label classifiers. The objective function is optimized in an alternating optimization fashion. Both theoretical analysis and experimental results on different multi-label image datasets verify the effectiveness and efficiency of the proposed method.

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Cited By

Liu SLiao QWang SLiu XZhu E(2024)Robust and Consistent Anchor Graph Learning for Multi-View ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336466336:8(4207-4219)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3364663
Yu SWang SWen YWang ZLuo ZZhu ELiu X(2023)How to Construct Corresponding Anchors for Incomplete Multiview ClusteringIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.331297934:4(2845-2860)Online publication date: 7-Sep-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3312979

Index Terms

Distance-Preserving Embedding Adaptive Bipartite Graph Multi-View Learning with Application to Multi-Label Classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning algorithms

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 2

February 2023

355 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3572847

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

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Publication History

Published: 20 February 2023

Online AM: 18 May 2022

Accepted: 08 May 2022

Revised: 01 May 2022

Received: 19 December 2021

Published in TKDD Volume 17, Issue 2

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National Natural Science Foundation of China
Beijing Natural Science Foundation
National Key Research and Development

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Cited By

Liu SLiao QWang SLiu XZhu E(2024)Robust and Consistent Anchor Graph Learning for Multi-View ClusteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336466336:8(4207-4219)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3364663
Yu SWang SWen YWang ZLuo ZZhu ELiu X(2023)How to Construct Corresponding Anchors for Incomplete Multiview ClusteringIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.331297934:4(2845-2860)Online publication date: 7-Sep-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3312979

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