Feature Dimension

Feature dimension, the number of variables used to represent data, is a critical aspect of machine learning impacting model performance and interpretability. Current research focuses on optimizing feature dimensionality, including techniques like dimensionality reduction to improve efficiency and interpretability, and methods to address issues arising from high-dimensional data, such as feature redundancy and the curse of dimensionality. These efforts are crucial for improving model generalization, particularly in challenging scenarios like imbalanced datasets and few-shot learning, and for enhancing the explainability of complex models in various applications, including medical image analysis and social network analysis.