A Nonparametric Data-Driven Classifier Based on the Cumulant Generating Function

Authors

Steven Kay, University of Rhode Island
Kaushallya Adhikari, University of Rhode Island
Bo Tang, School of Engineering

Document Type

Article

Date of Original Version

1-1-2025

Abstract

We introduce a nonparametric data-driven classifier that harnesses the statistical properties of the data through the cumulant generating function of the training data. Its implementation is straightforward, requiring only a single tuning parameter. Moreover, it ensures global solutions due to inherent convex optimization. The classifier is explainable, where unexpected or poor results can be interpreted and ameliorated. We derive the properties of the classification statistic, offering insightful observations. We apply the classifier to real-world datasets. The simulation results demonstrate the efficacy of the proposed classifier in signal classification, even in scenarios with mismatched training and testing datasets. Moreover, the results demonstrate that the CGFC has lower computational complexity compared to neural networks.

Publication Title, e.g., Journal

IEEE Transactions on Signal Processing

Volume

73

Citation/Publisher Attribution

Kay, Steven, Kaushallya Adhikari, and Bo Tang. "A Nonparametric Data-Driven Classifier Based on the Cumulant Generating Function." IEEE Transactions on Signal Processing 73, (2025). doi: 10.1109/TSP.2025.3525951.