Supervised low rank indefinite kernel approximation using minimum enclosing balls.
Frank-Michael SchleifAndrej GisbrechtPeter TiñoPublished in: Neurocomputing (2018)
Keyphrases
- low rank
- kernel matrix
- semi supervised
- kernel matrices
- low rank approximation
- matrix decomposition
- matrix factorization
- convex optimization
- linear combination
- eigendecomposition
- matrix completion
- low rank matrix
- missing data
- kernel learning
- rank minimization
- semidefinite programming
- singular value decomposition
- high dimensional data
- affinity matrix
- learning algorithm
- kernel methods
- supervised learning
- labeled data
- feature space
- trace norm
- data matrix
- singular values
- low rank matrices
- kernel pca
- high order
- active learning
- support vector
- feature selection
- reproducing kernel hilbert space
- approximation methods
- unsupervised learning
- collaborative filtering
- data sets
- multiple kernel learning
- metric learning
- kernel function
- minimization problems
- machine learning