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Feature selection and analysis on correlated gas sensor data with recursive feature elimination
Support vector machine recursive feature elimination (SVM-RFE) is a powerful feature
selection algorithm. However, when the candidate feature set contains highly correlated …
selection algorithm. However, when the candidate feature set contains highly correlated …
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 …
Deciduous forest responses to temperature, precipitation, and drought imply complex climate change impacts
Y **
In the high-dimensional regression setting, the elastic net produces a parsimonious model
by shrinking all coefficients toward the origin. However, in certain settings, this behavior …
by shrinking all coefficients toward the origin. However, in certain settings, this behavior …
Bayesian sparse group selection
This article proposes a Bayesian approach for the sparse group selection problem in the
regression model. In this problem, the variables are partitioned into different groups. It is …
regression model. In this problem, the variables are partitioned into different groups. It is …
The geometry of uniqueness, sparsity and clustering in penalized estimation
U Schneider, P Tardivel - Journal of Machine Learning Research, 2022 - jmlr.org
We provide a necessary and sufficient condition for the uniqueness of penalized least-
squares estimators whose penalty term is given by a norm with a polytope unit ball, covering …
squares estimators whose penalty term is given by a norm with a polytope unit ball, covering …
Graph-based regularization for regression problems with alignment and highly correlated designs
Sparse models for high-dimensional linear regression and machine learning have received
substantial attention over the past two decades. Model selection, or determining which …
substantial attention over the past two decades. Model selection, or determining which …
Clustered discriminant regression for high-dimensional data feature extraction and its applications in healthcare and additive manufacturing
The recent increase in applications of high-dimensional data poses a severe challenge to
data analytics, such as supervised classification, particularly for online applications. To …
data analytics, such as supervised classification, particularly for online applications. To …