Unsupervised Ensemble-Kernel Principal Component Analysis for Hyperspectral Anomaly Detection.
Nicholas MerrillColin C. OlsonPublished in: CVPR Workshops (2020)
Keyphrases
- anomaly detection
- hyperspectral
- kernel principal component analysis
- one class support vector machines
- unsupervised learning
- remote sensing
- kernel pca
- multispectral
- principal components
- discriminant analysis
- infrared
- feature extraction
- preprocessing
- feature space
- kernel function
- principal component analysis
- image data
- high dimensional
- supervised learning
- kernel methods
- kernel matrix
- face recognition
- semi supervised
- neural network
- feature vectors
- machine learning
- high dimensional feature space
- support vector
- training data
- ensemble methods
- dimensionality reduction
- co occurrence
- change detection
- training set
- feature selection
- data sets
- support vector machine svm
- learning algorithm
- computer vision
- spectral clustering
- information extraction