Feature Condensation

Feature condensation aims to reduce the dimensionality of data, specifically focusing on both the number of data points (e.g., nodes in a graph, frames in a video) and the feature dimensionality, to improve computational efficiency without significant performance loss. Current research explores various approaches, including structure-aware feature selection methods for graphs and deformation-aware pruning techniques for dynamic scenes, as well as addressing inherent limitations in existing normalization methods like batch normalization. This research is significant because it enables the application of computationally intensive models to larger datasets and resource-constrained environments, with applications ranging from graph neural networks to real-time video processing and efficient surgical scene reconstruction.