Class Manifold

Class manifolds represent the underlying low-dimensional structure of high-dimensional data, and research focuses on developing methods to effectively learn and utilize this structure for improved machine learning performance. Current efforts concentrate on developing algorithms for manifold segmentation, approximating functions and distributions on manifolds, and designing robust machine learning models that explicitly account for manifold geometry, often employing techniques like diffusion maps, normalizing flows, and geometric kernels. This research is significant because understanding and leveraging manifold structure improves the accuracy, robustness, and interpretability of machine learning models across diverse applications, including robotics, image processing, and scientific data analysis.