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Partial Multi-Label Learning With Noisy Label Identification.

Ming-Kun Xie, Sheng-Jun Huang

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    |February 15, 2021
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    Summary
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    This study introduces a novel partial multi-label learning (PML) method that simultaneously identifies and removes noisy labels by considering ambiguous content, improving classification accuracy in real-world applications.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Mining

    Background:

    • Partial multi-label learning (PML) assigns instances to candidate label sets containing relevant and noisy labels.
    • Existing methods often assume random noise generation, which is unrealistic in practice.
    • Noisy labels in PML are frequently due to ambiguous content within examples.

    Purpose of the Study:

    • To develop a novel partial multi-label learning approach for simultaneously recovering ground-truth labels and identifying noisy labels.
    • To address the limitations of existing disambiguation strategies that assume random noise generation.
    • To extend the proposed method to multi-instance multi-label learning by identifying noisy labels based on ambiguous instances.

    Main Methods:

    • A unified framework is proposed using trace norm and l1 norm regularizers to formalize the dual objectives of ground-truth recovery and noisy label identification.
    • Joint optimization of a multi-label classifier and a noisy label identifier is performed under the supervision of noise-corrupted labels.
    • Incorporation of label correlation exploitation and a feature-induced noise model.
    • Extension to multi-instance partial multi-label learning by mapping bags to feature vectors and identifying noisy labels based on ambiguous instances.

    Main Results:

    • The proposed approach effectively recovers ground-truth information and identifies noisy labels in partial multi-label learning tasks.
    • Experiments demonstrate the effectiveness of the method across multiple datasets and real-world applications.
    • The generalization bound is theoretically analyzed, supporting the method's robustness.

    Conclusions:

    • The developed partial multi-label learning approach offers a significant improvement over existing methods by accounting for content-induced noise.
    • The method provides a robust framework for handling noisy labels in complex multi-label learning scenarios.
    • The extension to multi-instance learning further broadens its applicability to diverse real-world problems.