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The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method.
The logistic regression model is penalized with the Sparse Group Lasso to fit the training data, and parameter estimation is obtained using the blockwise coordinate descent method and coordinate gradient descent method.
The logistic regression model penalized with Sparse Group Lasso is fitted on the training data, and parameter estimation is obtained by a blockwise coordinate descent method and coordinate gradient descent method.
The combined coordinate gradient descent method and blockwise coordinate method were then used to solve the objective function with a corresponding penalty term to get the parameter estimation results, based on the training data logistic regression model.
Additionally, we propose a method that combines the blockwise coordinate descent and coordinate gradient descent to estimate the parameters of the Sparse Group Lasso model, where nonzero model parameters signify that the corresponding feature or feature group is selected and vice versa.
Based on the literature [ 16], this paper proposes the Sparse Group Lasso method for channel selection and feature selection of the EEG fused feature and estimates model parameters using a combination of the blockwise coordinate descent method and the coordinate gradient descent method.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com