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Related Experiment Video

Updated: Feb 8, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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Fast Multiway Partial Least Squares Regression.

Flavio Camarrone, Marc M Van Hulle

    IEEE Transactions on Bio-Medical Engineering
    |July 12, 2018
    PubMed
    Summary

    Fast higher order partial least squares (fHOPLS) offers a computationally efficient multiway regression model for large datasets. This method demonstrates comparable accuracy to existing models while significantly reducing processing time for big data applications.

    Area of Science:

    • Data analysis
    • Machine learning
    • Signal processing

    Background:

    • Multiway array decomposition enhances data understanding and feature discovery for decoders.
    • High computational costs of traditional multiway algorithms limit their use in time-critical applications with large datasets.

    Purpose of the Study:

    • Introduce fast higher order partial least squares (fHOPLS), an optimized multiway regression model for large-scale tensors.
    • Evaluate the performance and computational efficiency of fHOPLS against existing methods.

    Main Methods:

    • Developed fast higher order partial least squares (fHOPLS) for efficient multiway regression.
    • Compared fHOPLS with higher order partial least squares (HOPLS), unfolded partial least squares (PLS), and linear regression using synthetic data.

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  • Assessed model performance in predicting electroencephalography (EEG) signals from electrocorticography (ECoG) data.

    • Main Results:

    • fHOPLS demonstrates significantly faster computation times than HOPLS, especially for large datasets.
    • The regression performance of fHOPLS and HOPLS is comparable and superior to unfolded PLS and linear regression.
    • Multiway array decoding provides more accurate results than traditional epoch-based averaging in brain-computer interfacing.

    Conclusions:

    • fHOPLS is a computationally efficient and accurate method for large-scale tensor regression.
    • fHOPLS offers a viable alternative to traditional methods in time-critical applications and big data scenarios.
    • Multiway array decoding shows promise for improved accuracy in brain-computer interface applications.