Prediction of magnetization dynamics in a reduced dimensional feature space setting utilizing a low-rank kernel method.
Lukas ExlNorbert J. MauserSebastian SchafferThomas SchreflDieter SuessPublished in: CoRR (2020)
Keyphrases
- kernel methods
- low rank
- feature space
- kernel matrix
- kernel function
- matrix factorization
- high dimensional feature space
- singular value decomposition
- kernel pca
- linear combination
- convex optimization
- missing data
- semidefinite programming
- rank minimization
- support vector machine
- support vector
- learning problems
- matrix completion
- low rank matrix
- semi supervised
- positive semidefinite
- multiple kernel learning
- kernel principal component analysis
- feature selection
- high dimensional data
- reproducing kernel hilbert space
- high order
- trace norm
- feature vectors
- dimensionality reduction
- machine learning
- input space
- data points
- recommender systems
- active learning
- singular values
- high dimensional
- data mining
- feature maps
- training set
- dimension reduction
- learning tasks
- training samples
- collaborative filtering