Input Dimension

Input dimension, the number of features used as input to a machine learning model, significantly impacts model performance and efficiency. Current research focuses on optimizing input dimension, exploring techniques like dimensionality reduction (e.g., PCA) to improve generalization and reduce computational costs in various architectures, including neural networks (e.g., GANs) and linear regression models. This optimization is crucial for handling high-dimensional data, improving model stability and accuracy, and enabling efficient training and deployment across diverse applications, from image processing to physical modeling.