- Title
- Multi-instance partial-label learning: towards exploiting dual inexact supervision
- Creator
- Tang, Wei; Zhang, Weijia; Zhang, Min-Ling
- Relation
- Science China Information Sciences Vol. 67, Issue 3, no. 132103
- Publisher Link
- http://dx.doi.org/10.1007/s11432-023-3771-6
- Publisher
- Zhongguo Kexue Zazhishe,Science in China Press
- Resource Type
- journal article
- Date
- 2024
- Description
- Weakly supervised machine learning algorithms are able to learn from ambiguous samples or labels, e.g., multi-instance learning or partial-label learning. However, in some real-world tasks, each training sample is associated with not only multiple instances but also a candidate label set that contains one ground-truth label and some false positive labels. Specifically, at least one instance pertains to the ground-truth label while no instance belongs to the false positive labels. In this paper, we formalize such problems as multi-instance partial-label learning (MIPL). Existing multi-instance learning algorithms and partial-label learning algorithms are suboptimal for solving MIPL problems since the former fails to disambiguate a candidate label set, and the latter cannot handle a multi-instance bag. To address these issues, a tailored algorithm named MiplGp, i.e., multi-instance partial-label learning with Gaussian processes, is proposed. MiplGp first assigns each instance with a candidate label set in an augmented label space, then transforms the candidate label set into a logarithmic space to yield the disambiguated and continuous labels via an exclusive disambiguation strategy, and last induces a model based on the Gaussian processes. Experimental results on various datasets validate that MiplGp is superior to well-established multi-instance learning and partial-label learning algorithms for solving MIPL problems.
- Subject
- machine learning; multi-instance partial-label learning; multi-instance learning; partial-label learning; gaussian processes
- Identifier
- http://hdl.handle.net/1959.13/1500688
- Identifier
- uon:54989
- Identifier
- ISSN:1674-733X
- Language
- eng
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