[2509.24140] A signal separation view of classification

Computer Science > Machine Learning arXiv:2509.24140 (cs) [Submitted on 29 Sep 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:A signal separation view of classification Authors:H. N. Mhaskar, Ryan O'Dowd View a PDF of the paper titled A signal separation view of classification, by H. N. Mhaskar and Ryan O'Dowd View PDF HTML (experimental) Abstract:The problem of classification in machine learning has often been approached in terms of function approximation. In this paper, we propose an alternative approach for classification in arbitrary compact metric spaces which, in theory, yields both the number of classes, and a perfect classification using a minimal number of queried labels. Our approach uses localized trigonometric polynomial kernels initially developed for the point source signal separation problem in signal processing. Rather than point sources, we argue that the various classes come from different probability measures. The localized kernel technique developed for separating point sources is then shown to separate the supports of these distributions. This is done in a hierarchical manner in our MASC algorithm to accommodate touching/overlapping class boundaries. We illustrate our theory on several simulated and real life datasets, including the Salinas and Indian Pines hyperspectral datasets and a document dataset. Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2509.24140 [cs.LG]   (or arXiv:2509.24140v2 [cs.LG] for th...