Netinfo Security ›› 2024, Vol. 24 ›› Issue (11): 1763-1772.doi: 10.3969/j.issn.1671-1122.2024.11.015

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Disambiguation-Based Partial Multi-Label Learning Algorithm Augmented by Fusing Instance and Label Correlations

GAO Guangliang(), LIANG Guangjun, HONG Lei, GAO Gugang, WANG Qun   

  1. Department of Computer Information and Cyber Security, Jiangsu Police Institute, Nanjing 210031, China
  • Received:2024-08-06 Online:2024-11-10 Published:2024-11-21

Abstract:

A set of candidate labels for each instance, which contains real and noisy labels, disambiguation-based partial multi-label learning aims to eliminate the noisy labels, thereby identifying and predicting the labels that are truly relevant to each instance. Traditional disambiguation strategies usually only focus on the correlation between labels and ignore the correlation between instances. To this end, a disambiguation-based partial multi-label learning algorithm augmented by fusing instance and label correlations was proposed, thereby improving the performance of disambiguation-based multi-label learning. First, a basic model was constructed based on the low-rank nature of ground-truth label matrix and the sparsity of noisy labels. Second, the kernel trick was used to map the feature vectors of the instances into a high-dimensional space so as to capture the linear and nonlinear correlations between the instances properly, which in turn helped us to eliminate noisy labels further. Finally, the associated labels of each instance was predicted by a linear mapping from the feature space to the label space. The experimental synthetic and real-world datasets show that compared with 8 comparative algorithms the algorithm proposed in the article has significant differences in statistics and performs better.

Key words: partial multi-label learning, instance correlation, label correlation, noisy label elimination

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