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Order-Preserving Optimal Transport for Distances between Sequences.

Bing Su, Gang Hua

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 21, 2018
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    Summary

    We developed novel distance measures for sequences, effectively handling temporal distortions and periodic patterns. These new methods improve sequence comparison by incorporating temporal information, enhancing machine learning model performance.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Sequence Analysis

    Background:

    • Comparing sequences with temporal distortions and periodicities is challenging.
    • Existing distance measures often fail to capture complex temporal dynamics.

    Purpose of the Study:

    • To introduce new sequence distance measures robust to local temporal distortion and arbitrary periodic starting points.
    • To frame sequence distance calculation as an optimal transport problem incorporating temporal information.

    Main Methods:

    • Viewing sequences as samples of an unknown distribution for optimal transport.
    • Incorporating temporal information via spatial ground metric and novel temporal regularization terms.
    • Utilizing inverse difference moment and KL-divergence regularization for temporal consistency.
    • Solving the optimal transport problems efficiently using matrix scaling algorithms.

    Main Results:

    • Demonstrated effectiveness across eight diverse datasets.
    • Showcased generality with different classifiers and performance metrics.
    • Validated the ability to handle local temporal distortion and periodic sequences.

    Conclusions:

    • The proposed distance measures offer a powerful and generalizable approach for sequence comparison.
    • These methods provide significant improvements in tasks involving complex temporal data.
    • The optimal transport framework with temporal regularization is effective for sequence analysis.