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On the optimization properties of the correntropic loss function in data analysis

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Abstract

Similarity measures play a critical role in the solution quality of data analysis methods. Outliers or noise often taint the solution, hence, practical data analysis calls for robust measures. The correntropic loss function is a smooth and robust measure. In this paper, we present the properties of the correntropic loss function that can be utilized in optimization based data analysis methods.

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Acknowledgments

This research is partially supported by NSF.

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32606, USA

    Mujahid N. Syed

  2. Department of Industrial and Systems Engineering, and Biomedical Engineering, University of Florida, Gainesville, FL, 32606, USA

    Panos M. Pardalos

  3. Department of Electrical and Biomedical Engineering, University of Florida, Gainesville, FL, 32606, USA

    Jose C. Principe

Authors
  1. Mujahid N. Syed
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  2. Panos M. Pardalos
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  3. Jose C. Principe
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Correspondence to Mujahid N. Syed.

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Syed, M.N., Pardalos, P.M. & Principe, J.C. On the optimization properties of the correntropic loss function in data analysis. Optim Lett 8, 823–839 (2014). https://doi.org/10.1007/s11590-013-0626-5

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  • DOI: https://doi.org/10.1007/s11590-013-0626-5

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