Robust Low-Rank Kernel Subspace Clustering based on the Schatten p-norm and Correntropy.
Xiaoqian ZhangBeijia ChenHuaijiang SunZhigui LiuZhenwen RenYanmeng LiPublished in: IEEE Trans. Knowl. Data Eng. (2020)
Keyphrases
- low rank
- rank minimization
- low rank representation
- kernel matrix
- low rank matrix
- kernel matrices
- high dimensional data
- linear combination
- low rank subspace
- convex optimization
- matrix factorization
- affinity matrix
- singular value decomposition
- missing data
- matrix completion
- eigendecomposition
- robust principal component analysis
- subspace clustering
- semi supervised
- feature space
- kernel learning
- matrix decomposition
- high order
- semidefinite programming
- low rank matrices
- support vector
- regularized regression
- pairwise
- learning algorithm
- singular values
- nonnegative matrix factorization
- high dimensional
- dimensionality reduction
- trace norm
- data points
- data analysis
- original data
- kernel methods
- small number
- collaborative filtering