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Deep Orientational Representation Learning for Ordinal Regression.

Gengyun Jia, Xin Ma, Bing-Kun Bao

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    Deep orientational representation learning (ORL) introduces directional characteristics for ordinal regression. This method ensures feature trajectories approximate geodesics, improving predictions for ordered classes like age estimation.

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    Area of Science:

    • Computer Science
    • Machine Learning

    Background:

    • Ordinal regression predicts ordered classes but underexplores directional characteristics in representation space.
    • Existing methods focus on label distribution shapes and feature distances, neglecting directional properties.

    Purpose of the Study:

    • To propose deep orientational representation learning (ORL) to capture directional characteristics for ordinal regression.
    • To ensure feature trajectories connected by ordinal categories approximate a geodesic in the representation space.

    Main Methods:

    • Introduced ORL, treating output layer weights as ordinal prototypes.
    • Implemented co-directional and counter-directional constraints on vector angles to optimize representations from different ordinal directions.
    • Extended ORL to a multi-prototype setting (MORL) to handle intra-class variations.

    Main Results:

    • Theoretical analysis links ORL to distribution unimodality and distance orderliness.
    • Demonstrated the effectiveness of ORL (MORL) on facial age estimation, historical image dating, and aesthetic quality assessment tasks.

    Conclusions:

    • ORL effectively captures directional information for improved ordinal regression.
    • The proposed method offers theoretical advantages and demonstrates practical utility across diverse ordered prediction tasks.