The proposed redundancy control penalty, which is designed to manage the selection of features while controlling redundancy, can be adapted to various machine learning models beyond neural networks by integrating it into the loss functions of these models. For instance, in linear models such as Support Vector Machines (SVM) or logistic regression, the redundancy control penalty can be added to the objective function to penalize the inclusion of highly correlated features. This can be achieved by modifying the regularization term to include a measure of dependency among features, similar to the approach taken in the neural network framework.
Additionally, ensemble methods like Random Forests or Gradient Boosting can incorporate the redundancy control penalty by adjusting the feature selection process during tree construction. By evaluating the correlation among features and applying a penalty for selecting redundant features, these models can enhance their interpretability and performance.
Moreover, the redundancy control mechanism can be implemented in unsupervised learning algorithms, such as clustering methods, where it can help in selecting representative features that minimize redundancy among clusters. This can lead to more meaningful cluster formations and improved model performance.