High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes.
David SalinasMichael Bohlke-SchneiderLaurent CallotRoberto MedicoJan GasthausPublished in: NeurIPS (2019)
Keyphrases
- low rank
- high dimensional
- high dimensional data
- dependence structure
- linear combination
- missing data
- convex optimization
- matrix completion
- matrix factorization
- low rank matrix
- marginal distributions
- rank minimization
- gaussian mixture
- singular value decomposition
- kernel matrix
- low dimensional
- dimensionality reduction
- low rank matrices
- matrix decomposition
- robust principal component analysis
- semi supervised
- high order
- trace norm
- input space
- singular values
- regression model
- sparse coding
- manifold learning
- gene expression data
- maximum likelihood
- nearest neighbor
- data points
- probability density function
- joint distribution
- neural network
- image denoising
- gaussian mixture model
- knn
- feature space