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End-to-End Signal Classification in Signed Cumulative Distribution Transform Space | IEEE Journals & Magazine | IEEE Xplore

End-to-End Signal Classification in Signed Cumulative Distribution Transform Space


Abstract:

This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT). We adopt a transport generative model to defi...Show More

Abstract:

This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT). We adopt a transport 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 computationally cheap, data efficient, and robust to out-of-distribution samples with respect to the existing end-to-end classification methods. The implementation of the proposed method in Python language is integrated as a part of the software package PyTransKit [1].
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 46, Issue: 9, September 2024)
Page(s): 5936 - 5950
Date of Publication: 01 March 2024

ISSN Information:

PubMed ID: 38427542

Funding Agency:


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