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Related Experiment Video

Updated: Feb 6, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

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Tensor Completion From One-Bit Observations.

Baohua Li, Xiaoning Zhang, Xiaoli Li

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 22, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new tensor completion method for noisy one-bit observations, improving upon squared loss functions. The novel optimization model effectively recovers underlying tensors using a convex relation to the tensor multi-rank.

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    Area of Science:

    • Signal Processing
    • Machine Learning
    • Optimization Theory

    Background:

    • Tensor completion is crucial in various fields, but traditional methods struggle with noisy one-bit data.
    • Existing data fidelity terms, like squared loss, are unsuitable for one-bit observations.

    Purpose of the Study:

    • To propose a novel optimization model for tensor completion with one-bit observations.
    • To address the limitations of squared loss functions in the presence of noisy one-bit data.

    Main Methods:

    • Developed a new optimization model leveraging the convex relation to the tensor multi-rank.
    • Proved the model's feasibility through theoretical derivations.
    • Designed an alternating direction method of multipliers (ADMM) based algorithm for efficient solution finding.

    Main Results:

    • The proposed model effectively recovers underlying tensors from noisy one-bit observations.
    • Theoretical derivations confirmed the model's feasibility.
    • Numerical experiments validated the method's effectiveness and superiority over existing approaches.

    Conclusions:

    • The novel optimization model offers a robust solution for tensor completion with one-bit data.
    • The developed ADMM-based algorithm provides an efficient way to solve the proposed model.
    • This work advances tensor completion techniques for scenarios with limited and noisy data.