Intrinsic Dimension Estimation-Based Feature Selection and Multinomial Logistic Regression for Classification of Bearing Faults Using Compressively Sampled Vibration Signals.
Hosameldin O. A. AhmedAsoke K. NandiPublished in: Entropy (2022)
Keyphrases
- feature selection
- monitoring and fault diagnosis
- fault diagnosis
- classification accuracy
- intrinsic dimension
- vibration signal
- feature set
- text classification
- support vector
- feature extraction
- intrinsic dimensionality
- feature space
- high dimensionality
- support vector machine
- dimensionality reduction
- fault detection
- multinomial logistic regression
- dimension reduction
- condition monitoring
- support vector machine svm
- image classification
- machine learning
- multi class
- feature vectors
- training set
- pattern recognition
- preprocessing
- model selection
- decision trees
- multiscale
- density estimation
- image processing
- unsupervised learning
- class labels
- expert systems
- data mining
- signal processing
- wavelet analysis
- operating conditions
- extracted features
- feature subset
- high dimensional
- nearest neighbor
- supervised learning