Confidence interval of singular vectors for high-dimensional and low-rank matrix regression.
Dong XiaPublished in: CoRR (2018)
Keyphrases
- low rank matrix
- confidence intervals
- singular value decomposition
- high dimensional
- low rank
- dimensionality reduction
- data matrix
- singular values
- gene expression data
- high dimensional data
- sample size
- low dimensional
- low rank approximation
- high dimensionality
- markov chain
- principal component analysis
- matrix factorization
- model selection
- original data
- feature space
- monte carlo
- microarray
- least squares
- support vector
- missing values
- missing data
- semi supervised
- gene expression
- conditional probabilities
- input space
- convex optimization
- kernel function
- data representation
- roc curve
- training samples
- data points
- subspace learning
- nonnegative matrix factorization
- linear combination
- machine learning