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Robust and Discriminative Labeling for Multi-Label Active Learning Based on Maximum Correntropy Criterion.

Bo Du, Zengmao Wang, Lefei Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 17, 2017
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    Summary

    This study introduces a robust multi-label active learning algorithm using Maximum Correntropy Criterion (MCC) to reduce labeling costs and improve model performance. The method effectively handles outlier labels for better uncertainty measurement in multi-label learning.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Artificial Intelligence

    Background:

    • Multi-label learning is crucial for real-world applications but incurs high costs for instance labeling.
    • Existing multi-label active learning methods struggle with outlier labels, impacting uncertainty measurement and model performance.
    • Diagnosing discriminative labels is challenging, hindering the development of effective multi-label models.

    Purpose of the Study:

    • To develop a robust multi-label active learning algorithm that simultaneously reduces labeling costs and enhances model training.
    • To address the challenge of outlier labels in uncertainty measurement within multi-label active learning.
    • To improve the accuracy and efficiency of multi-label learning by integrating uncertainty and representativeness.

    Main Methods:

    • Derivation of a robust multi-label active learning algorithm based on the Maximum Correntropy Criterion (MCC).
    • Implementation of an efficient alternating optimization method to solve the proposed algorithm.
    • Merging uncertainty and representativeness with predicted labels of unknown data for enhanced information measurement.

    Main Results:

    • The proposed method effectively eliminates the influence of non-discriminative outlier labels in uncertainty measurement.
    • Integrating uncertainty and representativeness with predicted labels enhances similarity measurement for multi-label data.
    • Experimental results on benchmark datasets demonstrate superior performance compared to state-of-the-art methods.

    Conclusions:

    • The MCC-based robust multi-label active learning algorithm offers a significant improvement over existing methods.
    • The approach effectively balances the reduction of labeling costs with enhanced model performance in multi-label learning.
    • This work provides a novel and efficient solution for handling outlier labels and improving information measurement in multi-label active learning.