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V2V: A Deep Learning Approach to Variable-to-Variable Selection and Translation for Multivariate Time-Varying Data.

Jun Han, Hao Zheng, Yunhao Xing

    IEEE Transactions on Visualization and Computer Graphics
    |October 19, 2020
    PubMed
    Summary
    This summary is machine-generated.

    We introduce V2V, a deep learning framework for variable-to-variable (V2V) translation in multivariate time-varying data (MTVD). V2V effectively identifies and maps relationships between variables for improved data analysis and visualization.

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    Area of Science:

    • Data Science
    • Machine Learning
    • Time Series Analysis

    Background:

    • Multivariate time-varying data (MTVD) analysis presents challenges in variable selection and translation.
    • Existing methods may not adequately capture complex relationships within MTVD.

    Purpose of the Study:

    • To develop a general-purpose deep learning framework, V2V, for the variable-to-variable (V2V) selection and translation problem in MTVD.
    • To enable accurate inference of unseen time steps for target variables based on source variables.

    Main Methods:

    • V2V utilizes representation learning to identify transferable variables.
    • Kullback-Leibler divergence is employed for source and target variable determination.
    • A generative adversarial network (GAN) with adversarial, volumetric, and feature losses learns the variable mapping.

    Main Results:

    • V2V demonstrates effectiveness in quantitatively and qualitatively analyzing diverse MTVD sets.
    • The framework successfully infers unseen time steps of target variables.
    • Comparative analysis shows V2V outperforming histogram matching, Pix2Pix, and CycleGAN.

    Conclusions:

    • V2V provides a robust and generalizable solution for V2V selection and translation in MTVD.
    • The framework enhances the analysis and visualization capabilities for complex time-varying datasets.