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Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection.

Shicheng Li1, Shumin Lai1, Yan Jiang1

  • 1School of Software, Jiangxi Normal University, Nanchang 330022, China.

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This study introduces a graph regularized deep sparse representation (GRDSR) for unsupervised anomaly detection. GRDSR enhances feature representation by incorporating deep learning and graph regularization, outperforming existing methods.

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Unsupervised anomaly detection (AD) identifies data points deviating from the norm.
  • Feature representation (FR) is crucial for AD performance.
  • Sparse representation (SR) is a powerful FR tool but has limitations.

Purpose of the Study:

  • To propose a novel Graph Regularized Deep Sparse Representation (GRDSR) approach for unsupervised anomaly detection.
  • To address limitations of traditional SR, including shallow feature extraction and ignored local geometry.
  • To improve the accuracy and effectiveness of anomaly detection.

Main Methods:

  • Developed a deep representation framework using multilayer matrix factorization for hierarchical feature extraction.
  • Introduced a graph regularization term to preserve local geometric structure and neighborhood relationships.
  • Incorporated a L1-norm sparsity constraint to enhance deep feature discriminability.
  • Utilized reconstruction error for anomaly identification.

Main Results:

  • The GRDSR approach effectively extracts hierarchical and geometrically aware features.
  • Experiments on ten datasets demonstrate superior performance compared to state-of-the-art methods.
  • The proposed method achieves the best performance in unsupervised anomaly detection tasks.

Conclusions:

  • GRDSR significantly advances unsupervised anomaly detection by integrating deep learning and graph regularization.
  • The method successfully captures complex data structures and enhances feature discriminability.
  • GRDSR offers a robust and effective solution for identifying anomalies in diverse datasets.