Métrique de distance Apprentissage (DML) est une sous-discipline de apprentissage automatique that focuses on learning a fonction de distance tailored for a specific task or dataset. Unlike traditional distance metrics, such as Euclidean or Manhattan distances, DML aims to optimize how distances between data points are calculated based on the characteristics of the data and the needs of the analysis.
The primary objective of DML is to improve the performance of machine learning algorithms, particularly in tasks such as classification, clustering, and retrieval. By learning a distance metric, the algorithm can better capture the underlying structure of the data, ensuring that similar data points are closer together in the learned space, while dissimilar points are further apart.
There are various approaches to DML, including supervised, semi-supervised, and unsupervised methods. In supervised DML, the algorithm is trained using données étiquetées, where the similarity or dissimilarity of pairs of examples is known. In contrast, unsupervised DML does not rely on labels and instead discovers the relationships within the data based solely on its inherent structure. Semi-supervised methods combine both labeled and unlabeled data to enhance the learning process.
Les algorithmes couramment utilisés en DML incluent la Contraste Loss, la Triplet Loss, et Nearest Neighbor à Large Marge (LMNN). Each of these approaches employs different strategies for adjusting the distance metric based on the training data. Overall, Distance Metric Learning is a powerful tool for improving the accuracy and efficiency of machine learning models by tailoring distance measures to the specific nuances of the data at hand.