Description: Orthogonal regularization is a technique used in the field of neural networks to prevent overfitting, a common problem in training machine learning models. This technique is based on imposing orthogonality among the model parameters, meaning that the weight vectors are sought to be perpendicular to each other. This property helps maintain diversity in the representations learned by the network, which in turn can improve the model’s ability to generalize to unseen data. Orthogonal regularization is often implemented as a penalty in the loss function during training, encouraging the network to learn representations that are not only effective for the specific task but also maintain an internal structure that avoids redundancy. This technique has become particularly relevant in deep learning architectures, where the complexity of the model can lead to significant overfitting. By promoting orthogonality, the aim is for each neuron in the network to capture unique and complementary information, which can result in more robust and efficient performance in classification, regression, and other machine learning applications.
