PCA and KPCA integrated Support Vector Machine for multi-fault classification.
Shen YinChen JingJian HouOkyay KaynakHuijun GaoPublished in: IECON (2016)
Keyphrases
- support vector machine
- kernel principal component analysis
- principal components analysis
- feature space
- svm classifier
- feature vectors
- support vector machine svm
- feature extraction
- principal component analysis
- kernel pca
- classification method
- kernel function
- support vector
- kernel methods
- principal components
- subspace methods
- feature selection
- classification algorithm
- face recognition
- machine learning
- dimensionality reduction
- svm classification
- decision boundary
- dimension reduction
- high dimensional
- training set
- linear discriminant analysis
- high classification accuracy
- preprocessing
- generalization ability
- image classification
- multi class
- training samples
- pattern recognition
- dimensionality reduction methods
- feature set
- principle component analysis
- decision trees
- small sample
- fault diagnosis
- classification accuracy
- dimension reduction methods
- kernel fisher discriminant
- face images
- decision forest
- soft margin
- training data
- kernel matrix
- pattern classification
- radial basis function
- independent component analysis
- knn
- text classification