metric learning
时间: 2023-09-15 14:23:53 浏览: 51
Metric learning is a type of machine learning technique that involves learning a metric, or a distance function, between pairs of data points in a dataset. The goal of metric learning is to learn a metric that can accurately capture the similarity or dissimilarity between pairs of data points, such that similar points are closer together in the learned metric space than dissimilar ones.
Metric learning has various applications in fields such as computer vision, natural language processing, and recommender systems. For example, in computer vision, metric learning can be used to learn a metric that can accurately measure the similarity between images, which can be used for tasks such as image retrieval or object recognition. In natural language processing, metric learning can be used to learn a metric that can measure the similarity between sentences or documents, which can be used for tasks such as text classification or information retrieval.
Some popular techniques for metric learning include siamese networks, triplet networks, and contrastive learning. These techniques involve learning a mapping function that maps input data points to a low-dimensional metric space, such that the distance between pairs of points in this space accurately reflects their similarity or dissimilarity.