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First off, you can do dimensionality reductiondimensionality reduction of features independent of any particular prediction problem, i.e. representation learning.

In the context of prediction problems, sparsity-promoting regularizationregularization can be used to automatically perform feature selectionfeature selection. This is commonly accomplished using $L_1$ penalties such as LASSOLASSO for linear regression (and also in deep learning).

($L_1$ regularization is also used in representation learning, such as sparse codingsparse coding and sparse autoencoders).

First off, you can do dimensionality reduction of features independent of any particular prediction problem, i.e. representation learning.

In the context of prediction problems, sparsity-promoting regularization can be used to automatically perform feature selection. This is commonly accomplished using $L_1$ penalties such as LASSO for linear regression (and also in deep learning).

($L_1$ regularization is also used in representation learning, such as sparse coding and sparse autoencoders).

First off, you can do dimensionality reduction of features independent of any particular prediction problem, i.e. representation learning.

In the context of prediction problems, sparsity-promoting regularization can be used to automatically perform feature selection. This is commonly accomplished using $L_1$ penalties such as LASSO for linear regression (and also in deep learning).

($L_1$ regularization is also used in representation learning, such as sparse coding and sparse autoencoders).

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GeoMatt22
GeoMatt22
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First off, you can do dimensionality reduction of features independent of any particular prediction problem, i.e. representation learning.

In the context of prediction problems, sparsity-promoting regularization can be used to automatically perform feature selection. This is commonly accomplished using $L_1$ penalties such as LASSO for linear regression (and more broadlyalso in sparse approximation /deep learning).

($L_1$ regularization is also used in representation learning, such as sparse coding and sparse autoencoders).

First off, you can do dimensionality reduction of features independent of any particular prediction problem, i.e. representation learning.

In the context of prediction problems, sparsity-promoting regularization can be used to automatically perform feature selection. This is commonly accomplished using $L_1$ penalties such as LASSO for regression (and more broadly in sparse approximation / sparse coding).

First off, you can do dimensionality reduction of features independent of any particular prediction problem, i.e. representation learning.

In the context of prediction problems, sparsity-promoting regularization can be used to automatically perform feature selection. This is commonly accomplished using $L_1$ penalties such as LASSO for linear regression (and also in deep learning).

($L_1$ regularization is also used in representation learning, such as sparse coding and sparse autoencoders).

Source Link
GeoMatt22
GeoMatt22
  • 13.1k
  • 3
  • 39
  • 72

First off, you can do dimensionality reduction of features independent of any particular prediction problem, i.e. representation learning.

In the context of prediction problems, sparsity-promoting regularization can be used to automatically perform feature selection. This is commonly accomplished using $L_1$ penalties such as LASSO for regression (and more broadly in sparse approximation / sparse coding).