Survey on distance metric learning and dimensionality reduction in data mining

• Published in: Data mining and knowledge discovery Vol. 29; no. 2; pp. 534 - 564
• Main Authors: Wang, Fei, Sun, Jimeng
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.03.2015; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Classification; Clustering; Computer Science; Data mining; Data Mining and Knowledge Discovery; Distance learning; Information Storage and Retrieval; Learning; Online tutorials; Physics; Spectra; Statistics for Engineering; Supervision; Tasks; Teaching methods; Texts; Dimensionality reduction; Data mining; Distance metric learning
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-014-0356-z

Distance metric learning is a fundamental problem in data mining and knowledge discovery. Many representative data mining algorithms, such as k -nearest neighbor classifier, hierarchical clustering and spectral clustering, heavily rely on the underlying distance metric for correctly measuring relations among input data. In recent years, many studies have demonstrated, either theoretically or empirically, that learning a good distance metric can greatly improve the performance of classification, clustering and retrieval tasks. In this survey, we overview existing distance metric learning approaches according to a common framework. Specifically, depending on the available supervision information during the distance metric learning process, we categorize each distance metric learning algorithm as supervised, unsupervised or semi-supervised . We compare those different types of metric learning methods, point out their strength and limitations. Finally, we summarize open challenges in distance metric learning and propose future directions for distance metric learning.