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Related Experiment Video

Updated: Feb 7, 2026

Fast and Accurate Exhaled Breath Ammonia Measurement
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CTD: Fast, accurate, and interpretable method for static and dynamic tensor decompositions.

Jungwoo Lee1, Dongjin Choi1, Lee Sael1

  • 1Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.

Plos One
|July 26, 2018
PubMed
Summary
This summary is machine-generated.

We developed CTD, a new tensor decomposition method for finding patterns and anomalies in multi-dimensional data. CTD is fast, accurate, interpretable, and efficient for real-time analysis, outperforming existing methods.

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Area of Science:

  • Data Science
  • Machine Learning
  • Cybersecurity

Background:

  • Detecting patterns and anomalies in multi-dimensional data (tensors) is crucial for applications like cybersecurity and network monitoring.
  • Existing tensor decomposition methods lack interpretability, efficiency, and speed for real-time, large-scale data analysis.

Purpose of the Study:

  • To propose a novel, fast, accurate, and directly interpretable tensor decomposition method for online pattern and anomaly detection.
  • To introduce both static (CTD-S) and dynamic (CTD-D) versions of the proposed method to handle diverse data streams.

Main Methods:

  • Developed CTD ( a tensor decomposition technique) utilizing efficient sampling for enhanced interpretability and performance.
  • Introduced CTD-S (static version) with provable accuracy improvements and significant gains in speed and memory efficiency.
  • Introduced CTD-D (dynamic version), the first interpretable dynamic tensor decomposition method, achieving substantial speedups through temporal factor exploitation.

Main Results:

  • CTD-S demonstrates up to 11x higher accuracy, 2.3x faster processing, and 24x more memory efficiency than state-of-the-art methods.
  • CTD-D achieves up to 82x speedup compared to CTD-S, enabling real-time analysis.
  • Successfully applied CTD for online distributed denial of service (DDoS) attack detection and online troll detection.

Conclusions:

  • CTD offers a significant advancement in interpretable tensor decomposition for real-time anomaly detection.
  • The proposed method addresses key limitations of existing techniques, providing a practical solution for large-scale, dynamic data.
  • CTD's effectiveness is validated through successful applications in critical cybersecurity scenarios.