Work

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

Year: 2025

Type: article

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.

Source: IEEE Transactions on Signal Processing

DOI: 10.1109/tsp.2025.3525951

Authors: Steven Kay, Kaushallya Adhikari, Bo Tang

Institutions: University of Rhode Island, Worcester Polytechnic Institute

Language: English

Topic: Wireless Signal Modulation Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,645

Funders: National Science Foundation, Air Force Office of Scientific Research