Exploiting low-rank covariance structures for computing high-dimensional normal and Student-t probabilities.
Jian CaoMarc G. GentonDavid E. KeyesGeorge M. TurkiyyahPublished in: Stat. Comput. (2021)
Keyphrases
- low rank
- high dimensional
- high dimensional data
- convex optimization
- missing data
- matrix factorization
- matrix completion
- low rank matrix
- linear combination
- low dimensional
- kernel matrix
- semi supervised
- singular value decomposition
- similarity search
- rank minimization
- high order
- matrix decomposition
- high dimensionality
- nearest neighbor
- trace norm
- dimensionality reduction
- data points
- learning environment
- singular values
- feature space
- learning process
- recommender systems
- non rigid structure from motion
- machine learning
- data sets
- robust principal component analysis
- input space
- manifold learning
- gene expression data
- covariance matrix
- least squares
- bayesian networks
- image segmentation
- data mining