Stochastic Low-Rank Kernel Learning for Regression.
Pierre MachartThomas PeelSandrine AnthoineLiva RalaivolaHervé GlotinPublished in: ICML (2011)
Keyphrases
- low rank
- kernel learning
- kernel matrix
- reproducing kernel hilbert space
- kernel matrices
- support vector regression
- semidefinite programming
- semi supervised
- linear combination
- matrix factorization
- convex optimization
- missing data
- singular value decomposition
- high dimensional data
- low rank matrix
- high order
- model selection
- regression model
- multiple kernel learning
- kernel function
- kernel methods
- gaussian processes
- machine learning
- denoising
- learning tasks
- markov random field
- collaborative filtering
- small number
- support vector machine
- data points
- feature extraction
- clustering algorithm