Scaling Up Kernel SVM on Limited Resources: A Low-Rank Linearization Approach.
Liang LanZhuang WangShandian ZheWei ChengJun WangKai ZhangPublished in: IEEE Trans. Neural Networks Learn. Syst. (2019)
Keyphrases
- limited resources
- low rank
- kernel matrix
- support vector
- kernel function
- kernel matrices
- kernel methods
- linear combination
- support vectors
- kernel machines
- convex optimization
- support vector machine svm
- missing data
- semi supervised
- support vector machine
- multiple kernel learning
- low rank matrix
- singular value decomposition
- matrix factorization
- kernel pca
- matrix completion
- kernel learning
- svm classifier
- feature space
- high dimensional data
- matrix decomposition
- rank minimization
- high order
- semidefinite programming
- polynomial kernels
- feature selection
- input space
- multi class
- low rank matrices
- hyperplane
- string kernels
- trace norm
- robust principal component analysis
- affinity matrix
- feature maps
- knn
- decision trees
- reproducing kernel hilbert space
- least squares
- small number