Stochastic Low-Rank Kernel Learning for Regression
Pierre MachartThomas PeelLiva RalaivolaSandrine AnthoineHervé GlotinPublished in: CoRR (2012)
Keyphrases
- low rank
- kernel matrix
- kernel learning
- reproducing kernel hilbert space
- kernel matrices
- semi supervised
- linear combination
- support vector regression
- semidefinite programming
- matrix factorization
- convex optimization
- low rank matrix
- missing data
- singular value decomposition
- regression model
- high order
- high dimensional data
- model selection
- kernel methods
- kernel function
- semi supervised learning
- multiple kernel learning
- metric learning
- pairwise constraints
- support vector
- image processing
- active learning
- feature space
- distance metric
- dimensionality reduction
- higher order
- decision trees