mlsanityData to 7 Oct 2026

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

arXiv 2205.00348 ↗First posted 30 Apr 2022eess.SP cs.LGcited by 7

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Abstract (arXiv)

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).

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 15 of its 65 references that are in the map. First posted on arXiv 30 Apr 2022.

Builds on

15 of 65 references.

References · 15

  1. 2018Deep learning for time series classification: a reviewcited by 575
  2. 2016Time Series Classification from Scratch with Deep Neural Networks: A Strong Baselinecited by 358
  3. 2017LSTM Fully Convolutional Networks for Time Series Classificationcited by 135
  4. 2017Deep Learning for Sensor-based Activity Recognition: A Surveycited by 133
  5. 2020An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networkscited by 128
  6. 2016Multi-Scale Convolutional Neural Networks for Time Series Classificationcited by 99
  7. 2013A Time Series Forest for Classification and Feature Extractioncited by 69
  8. 2018A review on distance based time series classificationcited by 40
  9. 2014Highly comparative feature-based time-series classificationcited by 39
  10. 2018Data augmentation using synthetic data for time series classification with deep residual networkscited by 38
  11. 2018Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approachcited by 15
  12. 2020Radon cumulative distribution transform subspace modeling for image classificationcited by 14
3 more
  1. 2021The Signed Cumulative Distribution Transform for 1-D Signal Analysis and Classificationcited by 9
  2. 2022Invariance encoding in sliced-Wasserstein space for image classification with limited training datacited by 7
  3. 2021Nearest Subspace Search in The Signed Cumulative Distribution Transform Space for 1D Signal Classificationcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 2; 2026: 1.

7 papers.

  1. 2023The Radon Signed Cumulative Distribution Transform and its applications in classification of Signed Imagescited by 1
  2. 2024Linear optimal transport subspaces for point set classificationcited by 1
  3. 2024Twisted Convolutional Networks (TCNs): Enhancing Feature Interactions for Non-Spatial Data Classificationcited by 1
  4. 2022Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Analysiscited by 0
  5. 2025An Efficient Transport-Based Dissimilarity Measure for Time Series Classification under Warping Distortionscited by 0
  6. 2025Improved Hyperspectral Anomaly Detection via Unsupervised Subspace Modeling in the Signed Cumulative Distribution Transform Domaincited by 0
  7. 20263D Transport-based Morphometry (3D-TBM) for medical image analysiscited by 0