Tying rotations of covariance matrices via riemannian subspace clustering.
Yusuke ShinoharaPublished in: ICASSP (2013)
Keyphrases
- subspace clustering
- covariance matrices
- log euclidean
- riemannian metric
- riemannian manifolds
- lie group
- covariance matrix
- high dimensional data
- maximum likelihood
- vector space
- clustering method
- distance measure
- subspace clusters
- high dimensional
- clustering algorithm
- gaussian mixture model
- gaussian distribution
- feature vectors
- manifold learning
- high dimensionality
- gaussian mixture
- euclidean space
- nearest neighbor
- linear classifiers
- low dimensional
- tensor field
- affine invariant
- shape analysis
- data sets
- geodesic distance
- dimensionality reduction
- semi supervised
- feature space
- computer vision