Linear discriminant analysis with sparse and dense signals

Cites Work

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Nearly unbiased variable selection under minimax concave penalty
A lava attack on the recovery of sums of dense and sparse signals
A well-conditioned estimator for large-dimensional covariance matrices
High dimensional discrimination analysis via a semiparametric model
Sparse linear discriminant analysis by thresholding for high dimensional data
High-dimensional classification using features annealed independence rules
Class prediction by nearest shrunken centroids, with applications to DNA microarrays.
Some theory for Fisher's linear discriminant function, `naive Bayes', and some alternatives when there are many more variables than observations
Sparse semiparametric discriminant analysis
QUADRO: a supervised dimension reduction method via Rayleigh quotient optimization
Penalized Classification using Fisher’s Linear Discriminant
A direct approach to sparse discriminant analysis in ultra-high dimensions
A Direct Estimation Approach to Sparse Linear Discriminant Analysis
Sparse Quadratic Discriminant Analysis For High Dimensional Data
Discriminant analysis through a semiparametric model
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Multiclass Sparse Discriminant Analysis
Applied Linear Regression
Covariance-enhanced discriminant analysis
Regularization and Variable Selection Via the Elastic Net
Model Selection and Estimation in Regression with Grouped Variables
Ridge Regression: Biased Estimation for Nonorthogonal Problems
A Road to Classification in High Dimensional Space: The Regularized Optimal Affine Discriminant
The elements of statistical learning. Data mining, inference, and prediction
A selective review of group selection in high-dimensional models
An Efficient Greedy Search Algorithm for High-Dimensional Linear Discriminant Analysis
Coordinatewise Gaussianization: Theories and Applications
The Elements of Statistical Learning

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