Robust Semi-supervised Multi-label Learning by Triple Low-Rank Regularization

Sun, Lijuan; Feng, Songhe; Lyu, Gengyu; Lang, Congyan

doi:10.1007/978-3-030-16145-3_21

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Semi-supervised multi-label learning (SS-ML) assumes that the training data comes in the form of a fully-labeled subset and an unlabeled subset [26,29,39,45,58]. This does not match our problem setting, where the set of labels applied to an instance may be incomplete.…”

Section: Related Workmentioning

confidence: 99%

“…While most approaches only study binary classification, some multi-label approaches emerged recently [1,11,23,27,41,46,52]. However, these new approaches make strict assumptions [39,49,51,58]. For instance, it is commonly assumed that a fully-labeled dataset is available alongside an unlabeled or MLML dataset [26,29,45], or some explicit negative labels are given (known as Explicit MLML) [48,50,55].…”

Section: Introductionmentioning

confidence: 99%

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Robust Recurrent Classifier Chains for Multi-Label Learning with Missing Labels

Gerych

Hartvigsen

Buquicchio

et al. 2022

Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management

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Recurrent Classifier Chains (RCCs) are a leading approach for multilabel classification as they directly model the interdependencies between classes. Unfortunately, existing RCCs assume that every training instance is completely labeled with all its ground truth classes. In practice often only a subset of an instance's labels are annotated, while the annotations for other classes are missing. RCCs fail in this missing label scenario, predicting many false negatives and potentially missing important classes. In this work, we propose Robust-RCC, the first strategy for tackling this open problem of RCCs failing for multi-label missing-label data. Robust-RCC is a new type of deep recurrent classifier chain empowered to model inter-class relationships essential for predicting the complete label set most likely to match the ground truth. The key to Robust-RCC is the design of the Multi Incomplete Label Risk (MILR) function, which we prove to be equal in expectation to the true risk of the ground truth full label set despite being computed from incompletely labeled data. Our experimental study demonstrates that Robust-RCC consistently beats six state-of-of-the-art methods by as much as 30% in predicting the true labels. CCS CONCEPTS• Theory of computation → Semi-supervised learning; • Computing methodologies → Neural networks.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Recurrent Classifier Chains for Multi-Label Learning with Missing Labels

Gerych

Hartvigsen

Buquicchio

et al. 2022

Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

show abstract

“…The current multi-label semi-supervised learning mainly includes the methods based on Graph Model [7], [8] and Double classifier structure [9]. In the study of graph-based methods, L. Jing et al [10] proposed semi-supervised low-rank Mapping Learning, forcing classifiers to be low-rank while adding a manifold regularization term to ensure data smoothness [9]. Then C. Gong et al [11] put forward the multi-label course learning of graph data based on the course learning strategy, and applied the course learning to graph data.…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-Label Semi-Supervised Learning Algorithm Based on Dual Selection Criteria

Liu,

Lu,

Shi

et al. 2024

IEEE Access

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With the rapid development of information technology, efficient multi label classification of massive data is one of the important tasks of big data systems. Semi supervised learning algorithm is an effective data classification method, currently mainly applied to the classification of single label data. This article proposes a multi label dynamic semi supervised learning algorithm based on dual selection criteria. The algorithm mainly establishes dual selection criteria for multi label pseudo labeled samples based on the COIN structure and K-nearest neighbor algorithm. A novel pseudo labeled sample selection method is designed, which improves the robustness and accuracy of the algorithm and effectively solves the problem of not considering sample correlation when selecting pseudo labeled samples. On this basis, by adding a performance evaluation mechanism to the model, the model can dynamically and adaptively extract pseudo labeled samples, improving the training speed and accuracy of the model. This article selected four convincing public test datasets for experiments, and the experimental results showed that the proposed semi supervised learning method has improved in multiple indicators such as robustness, accuracy, and training efficiency compared to current mainstream methods.

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Semi-supervised label enhancement via structured semantic extraction

Wen

Chen

et al. 2021

Int. J. Mach. Learn. & Cyber.

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Robust Semi-supervised Multi-label Learning by Triple Low-Rank Regularization

Cited by 5 publications

References 14 publications

Robust Recurrent Classifier Chains for Multi-Label Learning with Missing Labels

Robust Recurrent Classifier Chains for Multi-Label Learning with Missing Labels

Research on Multi-Label Semi-Supervised Learning Algorithm Based on Dual Selection Criteria

Semi-supervised label enhancement via structured semantic extraction

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