End-to-End Signal Classification in Signed Cumulative Distribution Transform Space
This is an incremental improvement for signal processing tasks, offering a transport-based generative model as an alternative to deep learning approaches.
The paper tackles signal classification by using the signed cumulative distribution transform (SCDT) to simplify the problem in a transform domain, solving it with a nearest local subspace search algorithm, resulting in high accuracy, data efficiency, robustness to out-of-distribution samples, and competitive computational complexity compared to deep learning methods.
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).