Spectral variable selection based on least absolute shrinkage and selection operator with ridge-adding homotopy
The least absolute shrinkage and selection operator (LASSO) is an established sparse
representation approach for variable selection, and its performance relies on finding a good
value for the regularization parameter, typically through cross-validation. However, cross-
validation is a computationally intensive step and requires a properly determined search
range and step size. In the present study, the ridge-adding homotopy (RAH) algorithm is
applied with LASSO to overcome the aforementioned shortcomings. The homotopy …
representation approach for variable selection, and its performance relies on finding a good
value for the regularization parameter, typically through cross-validation. However, cross-
validation is a computationally intensive step and requires a properly determined search
range and step size. In the present study, the ridge-adding homotopy (RAH) algorithm is
applied with LASSO to overcome the aforementioned shortcomings. The homotopy …
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