A Survey of L1 Regression
D Vidaurre, C Bielza… - International Statistical …, 2013 - Wiley Online Library
L1 regularization, or regularization with an L1 penalty, is a popular idea in statistics and
machine learning. This paper reviews the concept and application of L1 regularization for …
machine learning. This paper reviews the concept and application of L1 regularization for …
Alzheimer's disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions
Alzheimer's Disease (AD) is the most prevalent and rapidly progressing neurodegenerative
disorder among the elderly and is a leading cause of dementia. AD results in significant …
disorder among the elderly and is a leading cause of dementia. AD results in significant …
Stable learning via sample reweighting
We consider the problem of learning linear prediction models with model misspecification
bias. In such case, the collinearity among input variables may inflate the error of parameter …
bias. In such case, the collinearity among input variables may inflate the error of parameter …
A survey on sparse learning models for feature selection
X Li, Y Wang, R Ruiz - IEEE transactions on cybernetics, 2020 - ieeexplore.ieee.org
Feature selection is important in both machine learning and pattern recognition.
Successfully selecting informative features can significantly increase learning accuracy and …
Successfully selecting informative features can significantly increase learning accuracy and …
Trace lasso: a trace norm regularization for correlated designs
Abstract Using the $\ell_1 $-norm to regularize the estimation of the parameter vector of a
linear model leads to an unstable estimator when covariates are highly correlated. In this …
linear model leads to an unstable estimator when covariates are highly correlated. In this …
Sparse Prediction with the -Support Norm
We derive a novel norm that corresponds to the tightest convex relaxation of sparsity
combined with an $\ell_2 $ penalty. We show that this new norm provides a tighter …
combined with an $\ell_2 $ penalty. We show that this new norm provides a tighter …
Adaptive and weighted collaborative representations for image classification
R Timofte, L Van Gool - Pattern Recognition Letters, 2014 - Elsevier
Abstract Recently, Zhang et al.(2011) proposed a classifier based on Collaborative
Representations (CR) with Regularized Least Squares (CRC-RLS) for image face …
Representations (CR) with Regularized Least Squares (CRC-RLS) for image face …
Weighted elastic net penalized mean-variance portfolio design and computation
It is well known that the out-of-sample performance of Markowitz's mean-variance portfolio
criterion can be negatively affected by estimation errors in the mean and covariance. In this …
criterion can be negatively affected by estimation errors in the mean and covariance. In this …
Estimating the error variance in a high-dimensional linear model
The lasso has been studied extensively as a tool for estimating the coefficient vector in the
high-dimensional linear model; however, considerably less is known about estimating the …
high-dimensional linear model; however, considerably less is known about estimating the …