Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds.
Ming YinYi GuoJunbin GaoZhaoshui HeShengli XiePublished in: CoRR (2016)
Keyphrases
- subspace clustering
- riemannian manifolds
- high dimensional
- symmetric positive definite
- positive definite
- high dimensional data
- kernel function
- feature space
- manifold learning
- euclidean space
- kernel matrix
- low dimensional
- subspace clusters
- geometric structure
- reproducing kernel hilbert space
- input space
- parameter space
- high dimensionality
- clustering method
- sparse representation
- kernel methods
- clustering algorithm
- dimensionality reduction
- similarity search
- covariance matrix
- distribution function
- data points
- dimension reduction
- low rank
- text data
- vector space
- lie group
- higher dimensional
- mean shift
- nearest neighbor
- image processing
- geodesic distance
- kernel pca
- kernel machines
- principal component analysis
- feature extraction
- support vector
- support vector machine
- diffusion tensor
- gaussian processes
- data sets
- shape analysis