Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification on health datasets.

Agus Hartoyo¹, Jan Argasiński², Aleksandra Trenk³, Kinga Przybylska⁴, Anna Błasiak³, Alessandro Crimi⁵

¹Sano - Centre for Computational Personalised Medicine, International Research Foundation, Krakow, Poland; School of Computing, Telkom University, Bandung, Indonesia. Electronic address: a.hartoyo@sanoscience.org.

²Sano - Centre for Computational Personalised Medicine, International Research Foundation, Krakow, Poland; Department of Human-Centered Artificial Intelligence, Institute of Applied Computer Science, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow, Poland.

³Department of Neurophysiology and Chronobiology, Institute of Zoology and Biomedical Research, Faculty of Biology, Jagiellonian University, Krakow, Poland.

⁴Department of Neurophysiology and Chronobiology, Institute of Zoology and Biomedical Research, Faculty of Biology, Jagiellonian University, Krakow, Poland; Doctoral School of Exact and Natural Sciences, Jagiellonian University, Krakow, Poland.

⁵AGH University of Krakow, Krakow, Poland.

PubMed DOI

Abstract

Covariance and Hessian matrices have been analyzed separately in the literature for classification problems. However, integrating these matrices has the potential to enhance their combined power in improving classification performance. We present a novel approach that combines the eigenanalysis of a covariance matrix evaluated on a training set with a Hessian matrix evaluated on a deep learning mo…

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