Intrinsic dimension estimation-based feature selection and multinomial logistic regression for classification of bearing faults using compressively sampled vibration signals

HIGHLIGHTS

• who: Hosameldin O. A. Ahmed and Asoke K. Nandi from the Department of Mechanical and Aerospace Engineering, Brunel University London, London , PH, UK have published the paper: Intrinsic Dimension Estimation-Based Feature Selection and Multinomial Logistic Regression for Classification of Bearing Faults Using Compressively Sampled Vibration Signals, in the Journal: Entropy 2022, 24, 511. of /2022/
• what: This paper proposes an effective feature selection technique based on estimation of First compressive sampling (CS) is used to get compressed measurements from the collected raw Then a global estimator the geodesic minimal spanning tree (GMST) is employed . . .
  

   

  

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