Kernel-Based Subspace Learning on Riemannian Manifolds for Visual Recognition.
Xi LiuZhengming MaPublished in: Neural Process. Lett. (2020)
Keyphrases
- visual recognition
- subspace learning
- riemannian manifolds
- manifold learning
- euclidean space
- dimensionality reduction
- low dimensional
- image classification
- kernel pca
- principal component analysis
- geodesic distance
- kernel methods
- object recognition
- sparse coding
- feature space
- sparse representation
- high dimensional
- face recognition
- semi supervised
- parameter space
- geometric structure
- vector space
- reproducing kernel hilbert space
- dimension reduction
- high dimensional data
- feature extraction
- covariance matrices
- multi class classification
- support vector machine
- mean shift
- image features
- feature selection
- support vector
- data sets
- log euclidean
- covariance matrix
- image representation
- unsupervised learning
- data points
- probabilistic model
- similarity measure
- computer vision