Obtaining insights from high-dimensional data: sparse principal covariates regression.
Katrijn Van DeunElise A. V. CrompvoetsEva CeulemansPublished in: BMC Bioinform. (2018)
Keyphrases
- high dimensional data
- regression model
- high dimensional
- regression problems
- canonical correlation analysis
- sparse representation
- dimensionality reduction
- low dimensional
- nearest neighbor
- data sets
- high dimensions
- data analysis
- subspace clustering
- high dimensionality
- data points
- dimension reduction
- input space
- linear discriminant analysis
- manifold learning
- partial least squares
- similarity search
- sparse coding
- high dimensional datasets
- original data
- lower dimensional
- high dimensional spaces
- nonlinear dimensionality reduction
- clustering high dimensional data
- low rank
- gaussian process
- model selection
- random projections
- linear regression
- sample size
- response variable
- dimensional data
- image data
- data mining
- neural network
- high dimensional data sets
- real world
- subspace learning
- query processing
- dimensionality reduction methods
- input data
- underlying manifold
- gaussian processes