ET-Lasso: A New Efficient Tuning of Lasso-type Regularization for High-Dimensional Data.
Songshan YangJiawei WenXiang ZhanDaniel KiferPublished in: KDD (2019)
Keyphrases
- high dimensional data
- variable selection
- sparse representation
- group lasso
- low dimensional
- high dimensional
- dimensionality reduction
- nearest neighbor
- high dimensionality
- regularization parameter
- dimension reduction
- high dimensions
- data points
- similarity search
- feature selection
- linear regression
- data sets
- least squares
- low rank
- subspace clustering
- input space
- lower dimensional
- model selection
- data analysis
- manifold learning
- regression problems
- feature space
- data distribution
- decision trees
- high dimensional spaces
- variable weighting
- database
- data mining
- dimensional data
- training data
- pattern recognition
- cross validation