Manifold Regularization

Manifold regularization is a semi-supervised learning technique that improves model performance by leveraging the underlying geometric structure of data, particularly when labeled data is scarce. Current research focuses on enhancing manifold regularization within various machine learning contexts, including few-shot learning, federated learning, and tensor factorization, often employing techniques like diffusion maps, attention mechanisms, and Wasserstein distance to better capture data relationships and address challenges like overfitting and model inconsistency. These advancements lead to improved classification accuracy, more efficient training, and robust performance in diverse applications such as image generation, object pose estimation, and deep neural network training. The resulting models exhibit better generalization and stability, ultimately contributing to more reliable and efficient machine learning systems.