Incomplete label distribution learning via label correlation decomposition
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Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier b.v.
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Label 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.
Açıklama
Anahtar Kelimeler
Incomplete Annotation, Label Correlation, Label Distribution Learning, Low-Rank Structure, Sparsity
Kaynak
Information fusion
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
113
Sayı
Künye
Xu, S., Shang, L., Shen, F., Yang, X., & Pedrycz, W. (2025). Incomplete label distribution learning via label correlation decomposition. Information Fusion, 113, 102600.
