Compression, Inversion, and Approximate PCA of Dense Kernel Matrices at Near-Linear Computational Complexity.
Florian SchäferTimothy John SullivanHouman OwhadiPublished in: Multiscale Model. Simul. (2021)
Keyphrases
- linear computational complexity
- kernel matrices
- principal component analysis
- kernel methods
- kernel matrix
- linear complexity
- kernel function
- low rank
- kernel pca
- principal components analysis
- feature space
- computational complexity
- principal components
- face recognition
- image compression
- kernel learning
- singular value decomposition
- dimensionality reduction
- covariance matrix
- linear combination
- feature extraction
- compression ratio
- compression scheme
- generalization bounds
- high dimensional
- input space
- face images
- k means
- machine learning
- low rank matrix
- reinforcement learning
- support vector
- semidefinite programming
- multiple kernel learning
- support vector machine
- training algorithm
- low dimensional
- neural network