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Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget
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    This study introduces a robust support vector machine for novelty detection using worst-case risk minimization. The method effectively identifies unusual data points, outperforming existing models in experiments.

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    Area of Science:

    • Machine Learning
    • Statistical Learning Theory

    Background:

    • Novelty detection seeks to identify outliers or unusual data points.
    • Existing methods often struggle with uncertainty in data distributions.

    Purpose of the Study:

    • To propose a robust single-class support vector machine (SSVM) for novelty detection.
    • To develop a method based on worst-case conditional value-at-risk minimization.

    Main Methods:

    • The robust SSVM incorporates an uncertainty set for input data, leading to a maximization term similar to regularization.
    • Training is reformulated as a linear program for specific uncertainty sets (l1-norm, l∞-norm, box) and a second-order cone program for others (l2-norm, ellipsoidal).

    Main Results:

    • The proposed method demonstrates consistent statistical properties, with the estimated normal region converging to the true region under specific conditions.
    • Experimental results on three datasets show superior performance compared to three benchmark novelty detection models.

    Conclusions:

    • The robust SSVM offers a statistically consistent and effective approach to novelty detection.
    • The method's performance is validated through empirical evidence, highlighting its advantage over existing techniques.