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

作     者：Kay, Steven Adhikari, Kaushallya Tang, Bo 

作者机构：Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Worcester Polytech Inst Dept Elect & Comp Engn Worcester MA 01609 USA 

出 版 物：《IEEE TRANSACTIONS ON SIGNAL PROCESSING》 (IEEE Trans Signal Process)

年 卷 期：2025年第73卷

页      面：519-533页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：Air Force Office of Scientific Research [FA9550-24-1-0108] NSF [IIS-2325863] 

主　　题：Training data Probability density function Maximum likelihood estimation Vectors Smoothing methods Training Classification algorithms Testing Laplace equations Tuning Cumulant generating function data-driven classifier neural networks nonparametric classifier sonar data analysis 

摘      要：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.