Localized Lasso for High-Dimensional Regression.
Makoto YamadaKoh TakeuchiTomoharu IwataJohn Shawe-TaylorSamuel KaskiPublished in: AISTATS (2017)
Keyphrases
- high dimensional
- generalized linear models
- regression model
- linear regression
- variable selection
- ridge regression
- model selection
- group lasso
- regression coefficients
- regression methods
- low dimensional
- logistic regression
- multi variate
- regression problems
- high dimensionality
- regression algorithm
- linear models
- dimensionality reduction
- feature selection
- cross validation
- regression analysis
- high dimensional data
- least squares
- sparse representation
- estimation problems
- similarity search
- locally weighted
- high dimensional problems
- sparse data
- data points
- polynomial regression
- regularized regression
- support vector regression
- feature space
- nearest neighbor
- multi dimensional
- metric space
- dimension reduction
- microarray data
- neural network
- gaussian processes
- decision trees
- explanatory variables
- kernel function
- elastic net
- regression function
- noisy data
- low rank
- data sets
- simple linear
- reproducing kernel hilbert space
- partial least squares
- gaussian process
- regularization parameter