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Updated: Dec 23, 2025

Cross-Modal Multivariate Pattern Analysis
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Multilinear Compressive Learning.

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    Compressive learning (CL) now efficiently handles multidimensional data. Multilinear CL (MCL) preserves signal structure, outperforming vector-based methods in classification and recognition tasks.

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    Area of Science:

    • Multidimensional Signal Processing
    • Machine Learning
    • Data Science

    Background:

    • Compressive learning (CL) integrates compressive sensing (CS) and machine learning for inference on limited measurements.
    • Multidimensional data, like videos and hyperspectral images, possess inherent structural information often lost in traditional CL vectorization.

    Purpose of the Study:

    • Introduce multilinear compressive learning (MCL) to leverage the tensorial structure of multidimensional signals during acquisition.
    • Develop a CL framework that maintains signal structure for improved inference and efficiency.

    Main Methods:

    • Proposed a novel multilinear compressive learning (MCL) framework.
    • Designed a CL approach that incorporates signal's multidimensionality in the CS acquisition phase.
    • Built subsequent inference models directly on structurally acquired measurements.

    Main Results:

    • Theoretical analysis confirmed MCL's superior memory and computational efficiency over vector-based CL.
    • Empirical results demonstrated MCL's enhanced performance in object classification and face recognition.
    • MCL showed favorable scaling with increasing signal dimensionality, proving efficiency for high-dimensional data.

    Conclusions:

    • MCL effectively utilizes the inherent structure of multidimensional signals for compressive learning.
    • The proposed framework offers significant improvements in efficiency and performance for high-dimensional data analysis.
    • MCL represents a more suitable approach for machine learning tasks involving structured multidimensional data.