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Semi-Supervised SVM With Extended Hidden Features.

Aimei Dong, Fu-Lai Chung, Zhaohong Deng

    IEEE Transactions on Cybernetics
    |November 17, 2015
    PubMed
    Summary
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    This study introduces a novel semi-supervised support vector machine with extended hidden features (SSVM-EHF). This method enhances classification accuracy by avoiding direct use of unlabeled data, preventing error propagation from mislabeled samples.

    Area of Science:

    • Machine Learning
    • Computer Science

    Background:

    • Traditional semi-supervised learning (SSL) algorithms often use automated labeling of unlabeled data.
    • Falsely labeled data in SSL can negatively impact classifier performance due to error propagation.
    • Direct incorporation of unlabeled samples during automated labeling poses risks in SSL.

    Purpose of the Study:

    • To propose a new semi-supervised support vector machine with extended hidden features (SSVM-EHF).
    • To address the issue of error propagation caused by falsely labeled data in SSL.
    • To develop a method that enhances classifier robustness by modifying the training strategy.

    Main Methods:

    • The SSVM-EHF extends sample dimensionality using orthonormal transformation to create shared hidden features.
    • It utilizes the maximum margin principle and minimizes integrated squared error between probability distributions.

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  • Training is performed exclusively on labeled samples using original and hidden features, excluding explicit use of unlabeled samples.

    • Main Results:

    • Experimental results demonstrate the effectiveness of the proposed SSVM-EHF method.
    • The approach successfully mitigates the negative impact of potential mislabeled samples.
    • The method achieves improved classification performance compared to traditional SSL techniques.

    Conclusions:

    • The SSVM-EHF offers a robust solution for semi-supervised learning, particularly when dealing with noisy labels.
    • By avoiding direct use of unlabeled data in automated labeling, the method prevents error propagation.
    • The proposed technique provides a more reliable approach to semi-supervised classification.