Kernel Coherence Pursuit: A Manifold Learning-based Outlier Detection Technique.
Mahlagha SedghiGeorge K. AtiaMichael GeorgiopoulosPublished in: ACSSC (2018)
Keyphrases
- outlier detection
- manifold learning
- laplacian eigenmaps
- feature space
- low dimensional
- dimensionality reduction
- high dimensional
- knowledge discovery
- nonlinear dimensionality reduction
- semi supervised
- detecting outliers
- kernel function
- density ratio estimation
- detection algorithm
- data streams
- density estimation
- dimension reduction
- kernel pca
- data mining
- high dimensional data
- detect outliers
- kernel methods
- input space
- feature extraction
- sparse representation
- kernel machines
- support vector
- embedding space
- database
- active learning
- similarity measure
- kernel matrix
- pattern recognition
- pairwise
- labeled data