Nearest Neighbor

Nearest neighbor (NN) methods aim to find data points most similar to a given query, a fundamental task in numerous machine learning applications. Current research focuses on improving the efficiency and accuracy of approximate NN search (ANN) through techniques like tensor-train decompositions for data compression, graph neural networks for decentralized decision-making, and adaptive k-NN algorithms that adjust the number of neighbors based on local data characteristics. These advancements are crucial for handling increasingly large and complex datasets, impacting fields ranging from medical image analysis and drug discovery to robotics and natural language processing. The development of efficient and robust ANN methods continues to be a significant area of investigation, driving improvements in various applications.