Nearest neighbor search (NNS) is an algorithmic problem of finding the point (or set of points) closest to a given query point in a database or dataset. This concept is very important in many fields, including machine learning, data mining, computer vision, and geographic information systems. In machine learning, nearest neighbor search is the core of the k-nearest neighbor (k-NN) algorithm, which performs classification or regression analysis by finding the training samples that are most similar to the unknown samples. In the field of computer vision, it is used for feature matching and object recognition by comparing feature vectors to find the best matching image or feature point. In geographic information systems, nearest neighbor search helps to determine relevant entities near a specific geographic location, such as searching for the nearest restaurant or gas station.
One of the main challenges facing nearest neighbor search is the processing of high-dimensional data, which is known as the "curse of dimensionality". As the dimension of the data increases, the distance differences between data points become less obvious, which causes the search efficiency to drop sharply. In addition, as the size of the dataset grows, the cost of calculating the nearest neighbors also increases. To address these problems, researchers have developed a variety of index structures, such as kd-trees and R-trees, which can significantly improve search efficiency.