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

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 d...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2024-09, Vol.46 (9), p.5936-5950
Main Authors: Rubaiyat, Abu Hasnat Mohammad, Li, Shiying, Yin, Xuwang, Shifat-E-Rabbi, Mohammad, Zhuang, Yan, Rohde, Gustavo K.
Format: Article
Language:English
Subjects:
Convolutional neural networks
Data mining
Data models
Feature extraction
nearest local subspace
Pattern classification
SCDT
Signal classification
Time series analysis
time series events
Transforms
transport generative model
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Summary: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].
ISSN:0162-8828
1939-3539
1939-3539
2160-9292
DOI:10.1109/TPAMI.2024.3372455