Stochastic Neighbor Embedding

Stochastic Neighbor Embedding (SNE) techniques are dimensionality reduction methods aiming to visualize high-dimensional data while preserving local neighborhood structures. Current research focuses on improving SNE's efficiency for large datasets, particularly through hierarchical approaches and optimized algorithms like t-SNE and its variants, as well as integrating SNE with other machine learning paradigms such as federated learning and contrastive learning. These advancements are significantly impacting fields like computer vision, graph analysis, and data visualization by enabling efficient exploration and analysis of complex, high-dimensional data.