Weaker Regularity Conditions and Sparse Recovery in High-Dimensional Regression.
Shiqing WangYan ShiLimin SuPublished in: J. Appl. Math. (2014)
Keyphrases
- high dimensional
- sparse data
- generalized linear models
- low dimensional
- regression model
- dimensionality reduction
- high dimensionality
- sufficient conditions
- sparse coding
- high dimension
- relevance vector machine
- sparse regression
- sparse bayesian learning
- similarity search
- estimation problems
- data points
- sparse kernel
- feature space
- high dimensional problems
- linear regression
- multi variate
- data sets
- variable selection
- input space
- dimension reduction
- nearest neighbor
- support vector regression
- parameter space
- compressive sensing
- regression method
- additive models
- high dimensional data
- regression algorithm
- support vector
- machine learning
- polynomial regression
- regularized regression
- locally weighted
- covariance function
- gaussian process regression
- canonical correlation analysis
- regression problems
- manifold learning
- microarray data
- gene expression data
- neural network