  This paper presents a new end-to-end signal classification method using the
signed cumulative distribution transform (SCDT). We adopt a transport-based
generative model to define the classification problem. We then make use of
mathematical properties of the SCDT to render the problem easier in transform
domain, and solve for the class of an unknown sample using a nearest local
subspace (NLS) search algorithm in SCDT domain. Experiments show that the
proposed method provides high accuracy classification results while being data
efficient, robust to out-of-distribution samples, and competitive in terms of
computational complexity with respect to the deep learning end-to-end
classification methods. The implementation of the proposed method in Python
language is integrated as a part of the software package PyTransKit
(https://github.com/rohdelab/PyTransKit).
