Partial Multi-Label Learning with Global and Local Manifold Disambiguation

DOI

10.2991/978-94-6463-040-4_203How to use a DOI?

Keywords

Partial Multi-Label Learning; Disambiguation; Manifold

Abstract

In Partial Multi-Label learning (PML), each training example is assigned with a candidate label set where only partial labels are correct. Existing PML methods only focus on global label correlation, while they lack the consideration of the local label correlation. To alleviate this issue, a novel framework is proposed to jointly consider the feature manifold structure over the global instances and local instances. Specifically, we firstly explore the global feature manifold and local feature manifold by the affinity information conveyed by feature vectors. Then, a trade-off parameter is introduced to character the relative contribution of the feature manifold structures optimized by different methods. Afterwards, in order to disambiguate the candidate labels, we utilize the joint feature manifold in the label space. Finally, the predicted results are learned by training a linear multi-label classification model. Extensive experiments on six PML datasets demonstrate the effectiveness of our proposed method.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Qizheng Pan
AU  - Jianmin Li
AU  - Ying Ma
PY  - 2022
DA  - 2022/12/27
TI  - Partial Multi-Label Learning with Global and Local Manifold Disambiguation
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 1364
EP  - 1370
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_203
DO  - 10.2991/978-94-6463-040-4_203
ID  - Pan2022
ER  -