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A low-rank approximation-based transductive support tensor machine for semisupervised classification.

Xiaolan Liu, Tengjiao Guo, Lifang He

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 21, 2015
    PubMed
    Summary
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    This study introduces a faster method for semisupervised tensor classification, called concave-convex procedure-based transductive support vector machine (CCCP-TSTM). CCCP-TSTM significantly improves test accuracy and training speed compared to existing methods.

    Area of Science:

    • Machine learning
    • Pattern recognition
    • Computer vision
    • Image processing

    Background:

    • Data in machine learning is often represented by tensors.
    • Existing transductive support tensor machine (TSTM) methods for semisupervised classification are time-consuming due to iterative techniques.

    Purpose of the Study:

    • To propose a faster and more accurate semisupervised tensor classification method.
    • To extend the concave-convex procedure-based transductive support vector machine (CCCP-TSVM) to tensor data.

    Main Methods:

    • Developed a low-rank approximation-based TSTM using tensor rank-one decomposition for inner product computation.
    • Extended the linear CCCP-TSVM to tensor patterns, creating CCCP-TSTM.
    • CCCP-TSTM degenerates to linear CCCP-TSVM for vector data.

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    Main Results:

    • CCCP-TSTM demonstrated significant performance gains on 23 semisupervised classification tasks.
    • Experiments were conducted on face, gait, and image datasets.
    • Achieved improvements in both test accuracy and training speed compared to CCCP-TSVM and TSTM.

    Conclusions:

    • CCCP-TSTM offers a more efficient and accurate approach for semisupervised tensor classification.
    • The proposed method effectively handles tensor patterns in machine learning tasks.
    • This advancement benefits fields like pattern recognition and computer vision.