Scaling up Kernel SVM on Limited Resources: A Low-rank Linearization Approach.
Kai ZhangLiang LanZhuang WangFabian MoerchenPublished in: AISTATS (2012)
Keyphrases
- limited resources
- low rank
- kernel matrix
- kernel function
- support vector
- kernel matrices
- kernel methods
- linear combination
- missing data
- support vector machine svm
- matrix factorization
- matrix completion
- convex optimization
- multiple kernel learning
- kernel pca
- support vectors
- kernel learning
- semidefinite programming
- support vector machine
- high dimensional data
- rank minimization
- svm classifier
- singular value decomposition
- low rank matrix
- semi supervised
- kernel machines
- polynomial kernels
- feature space
- high order
- knn
- kernel principal component analysis
- matrix decomposition
- reproducing kernel hilbert space
- feature vectors
- multi class
- trace norm
- machine learning
- feature selection
- string kernels
- feature maps
- hyperplane
- input space
- singular values
- linear svm
- nearest neighbor
- high dimensional
- robust principal component analysis
- ls svm
- loss function
- minimization problems
- training samples
- pairwise
- training set
- face recognition