Low-Rank Kernel Learning with Bregman Matrix Divergences.
Brian KulisMátyás A. SustikInderjit S. DhillonPublished in: J. Mach. Learn. Res. (2009)
Keyphrases
- low rank
- kernel learning
- kernel matrix
- kernel matrices
- semi supervised
- convex optimization
- positive semidefinite
- low rank matrix
- bregman divergences
- singular value decomposition
- missing data
- linear combination
- matrix factorization
- semidefinite programming
- total variation
- high order
- matrix completion
- multiple kernel learning
- low rank approximation
- kernel function
- high dimensional data
- image restoration
- kernel methods
- semi supervised learning
- supervised learning
- support vector regression
- cost sensitive
- image processing
- learning tasks
- dimensionality reduction
- collaborative filtering
- feature space
- similarity measure
- decision trees