research-article

Prior Knowledge Constrained Adaptive Graph Framework for Partial Label Learning

Authors:

Zhen YangAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 2

Article No.: 25, Pages 1 - 16

https://doi.org/10.1145/3569421

Published: 16 February 2023 Publication History

Abstract

Partial label learning (PLL) aims to learn a robust multi-class classifier from the ambiguous data, where each instance is given with several candidate labels, among which only one label is real. Most existing methods usually cope with such problem by utilizing a feature similarity graph to conduct label disambiguation. However, these methods construct the feature graph by only employing original features, while the influences of latent outliers and the contributions of label space are regrettably ignored. To tackle these issues, in this article, we propose a Prior KnOwledge ConsTrained Adaptive Graph FramEwork (POTAGE) for partial label learning, which utilizes an adaptive graph fused with label information to accurately describe the instance relationship and guide the desired model training. Compared with the feature-induced fixed graph, the adaptive graph is deemed to be more robust and accurate to reveal the intrinsic manifold structure within the data, and the embedding label information is expected to effectively alleviate the label ambiguities and enlarge the gap of label confidences between two instances from different classes. Extensive experiments demonstrate that POTAGE achieves state-of-the-art performance.

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Cited By

Patel RMalhotra AModak SYeramsetty SSerra ESpezzano F(2024)Progressive Label Disambiguation for Partial Label Learning in Homogeneous GraphsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679982(3969-3973)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679982
Zhao LXiao YLiu B(2024)Partial label learning with heterogeneous domain adaptationNeurocomputing10.1016/j.neucom.2024.127822594(127822)Online publication date: Aug-2024
https://doi.org/10.1016/j.neucom.2024.127822

Index Terms

Prior Knowledge Constrained Adaptive Graph Framework for Partial Label Learning

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 2

April 2023

430 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3582879

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 February 2023

Online AM: 25 October 2022

Accepted: 17 October 2022

Revised: 01 September 2022

Received: 07 December 2021

Published in TIST Volume 14, Issue 2

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Fundamental Research Funds for the Central University
National Natural Science Foundation of China
Beijing Natural Science Foundation
Major Research Plan of National Natural Science Foundation of China
Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions
National Key Research and Development Project

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Cited By

Patel RMalhotra AModak SYeramsetty SSerra ESpezzano F(2024)Progressive Label Disambiguation for Partial Label Learning in Homogeneous GraphsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679982(3969-3973)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679982
Zhao LXiao YLiu B(2024)Partial label learning with heterogeneous domain adaptationNeurocomputing10.1016/j.neucom.2024.127822594(127822)Online publication date: Aug-2024
https://doi.org/10.1016/j.neucom.2024.127822

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