  This paper presents a new method to classify 1D signals using the signed
cumulative distribution transform (SCDT). The proposed method exploits certain
linearization properties of the SCDT to render the problem easier to solve in
the SCDT space. The method uses the nearest subspace search technique in the
SCDT domain to provide a non-iterative, effective, and simple to implement
classification algorithm. Experiments show that the proposed technique
outperforms the state-of-the-art neural networks using a very low number of
training samples and is also robust to out-of-distribution examples on
simulated data. We also demonstrate the efficacy of the proposed technique in
real-world applications by applying it to an ECG classification problem. The
python code implementing the proposed classifier can be found in PyTransKit
(https://github.com/rohdelab/PyTransKit).
