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    Feature level sets (FLS) simplify multi-field data analysis. New methods improve trait design and use trait-induced merge trees (TIMTs) for efficient feature selection and topological analysis.

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    Area of Science:

    • Computer Graphics
    • Data Visualization
    • Scientific Computing

    Background:

    • Feature level sets (FLS) offer potential for multi-field data analysis.
    • Practical application of FLS faces challenges in trait design and feature selection.

    Purpose of the Study:

    • To simplify trait design for feature level sets.
    • To enhance feature selection and topological analysis of multi-variate data.
    • To introduce trait-induced merge trees (TIMTs) as a generalization of merge trees for FLS.

    Main Methods:

    • Propose a Cartesian decomposition for intuitive and efficient trait design.
    • Utilize dictionary learning to automatically suggest point traits.
    • Introduce TIMTs for topological analysis, generalizing merge trees for FLS.
    • Develop query techniques for TIMTs to identify relevant and persistent features.

    Main Results:

    • Simplified and computationally efficient trait design process.
    • Automatic suggestion of point traits using dictionary learning.
    • TIMTs enable topological analysis and hierarchical feature querying.
    • Demonstrated cross-application utility across five diverse domains.

    Conclusions:

    • The proposed methods significantly improve the practical usability of feature level sets.
    • Trait-induced merge trees provide a robust framework for topological analysis of complex datasets.
    • The approach is versatile and applicable to various scientific domains.