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NEAREST SUBSPACE SEARCH IN THE SIGNED CUMULATIVE DISTRIBUTION TRANSFORM SPACE FOR 1D SIGNAL CLASSIFICATION

Citation Author(s):
Abu Hasnat Mohammad Rubaiyat, Mohammad Shifat-E-Rabbi, Yan Zhuang, Shiying Li, Gustavo K. Rohde
Submitted by:
Abu Hasnat Moha...
Last updated:
5 May 2022 - 7:15pm
Document Type:
Presentation Slides
Document Year:
2022
Event:
Presenters:
Abu Hasnat Mohammad Rubaiyat
Paper Code:
MLSP-15.1
 

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.

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