Incomplete label distribution learning via label correlation decomposition

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorXu, Suping
dc.contributor.authorShang, Lin
dc.contributor.authorShen, Furao
dc.contributor.authorYang, Xibei
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2025-04-17T10:35:46Z
dc.date.available2025-04-17T10:35:46Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractLabel distribution learning (LDL) has garnered increased attention in recent studies on label ambiguity. However, collecting complete annotations for LDL tasks is often time-consuming and labor-intensive compared to traditional learning paradigms. Therefore, designing effective incomplete LDL algorithms is crucial to broaden LDL's application scope. In this paper, we propose a novel LDL algorithm, called Incom plete L abel D istribution L earning via L abel C orrelation D ecomposition (IncomLDL-LCD), which simultaneously learns label distributions and recovers missing description degrees of labels through label correlations. Specifically, we decompose the label correlation into sparse local label correlation and low-rank global label correlation using a softthresholding operator and a singular value thresholding operator, respectively. The former is utilized to capture the related label subsets necessary for reconstructing each possible label, while the latter focuses on extracting the coarse-grained semantic concepts from all labels and exploring the groupings of labels. Additionally, we develop an alternating solution with the accelerated proximal gradient descent method for optimization. Extensive experiments on 16 real-world data sets with varying degrees of missing annotations validate that our algorithm effectively handles incomplete LDL tasks and outperforms state-of-the-art algorithms.
dc.description.sponsorshipNational Natural Science Foundation of China
dc.identifier.citationXu, S., Shang, L., Shen, F., Yang, X., & Pedrycz, W. (2025). Incomplete label distribution learning via label correlation decomposition. Information Fusion, 113, 102600.
dc.identifier.doi10.1016/j.inffus.2024.102600
dc.identifier.endpage17
dc.identifier.issn1566-2535
dc.identifier.issn1872-6305
dc.identifier.scopus2-s2.0-85200355728
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.inffus.2024.102600
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6200
dc.identifier.volume113
dc.identifier.wosWOS:001290206500001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherElsevier b.v.
dc.relation.ispartofInformation fusion
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectIncomplete Annotation
dc.subjectLabel Correlation
dc.subjectLabel Distribution Learning
dc.subjectLow-Rank Structure
dc.subjectSparsity
dc.titleIncomplete label distribution learning via label correlation decomposition
dc.typeArticle

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