Distance Metric Learning Task
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A Distance Metric Learning Task is a learning task that produces a distance metric.
References
2008
- (Xiang & al, 2008) ⇒ Shiming Xiang, Feiping Nie, and Changshui Zhang. (2008). "Learning a Mahalanobis Distance Metric for Data Clustering and Classification." In: Pattern Recognition 41.
- There has been considerable research on distance metric learning over the past few years [14]. One family of algorithms are developed with known class labels of training data points. Algorithms in this family include the neighboring component analysis [15], large margin nearest neighbor classification [16], large margin component analysis [17], class collapse [18], and other extension work [19,20]. The success in a variety of problems shows that the learned distance metric yields substantial improvements over the commonly used Euclidean distance metric [15–18]. However, class label may be strong information from the users and cannot be easily obtained in some real-world situations. In contrast, it is more natural to specify which pairs of data points are similar or dissimilar. Such pairwise constraints appear popularly in many applications. For example, in image retrieval the similar and dissimilar images to the query one are labeled by the user and such image pairs can be used to learn a distance metric [21]. Accordingly, another family of distance metric learning algorithms are developed to make use of such pairwise constraints [14,21–29]. Pairwise constraint is a kind of side information [22]. One popular form of side information is must-links and cannot-links [22,30–35]. A must-link indicates the pair of data points must be in a same class, while a cannot-link indicates that the two data points must be in two different classes. Another popular form is the relative comparison with “A is closer to B than A is to C” [26]. The utility of pairwise constraints has been demonstrated in many applications, indicating that significantly improvement of the algorithm can be achieved [21–27].
2006
- (Yang & Jin, 2006) ⇒ Liu Yang, and Rong Jin. (2006). "Distance Metric Learning: A comprehensive survey." In: Technical Report, Michigan State University.
2004
- (Bilenko & al, 2004) ⇒ Mikhail Bilenko, Sugato Basu, and Raymond Mooney. (2004). "Integrating Constraints and Metric Learning in Semi-Supervised Clustering." In: Proceedings of the twenty-first international conference on Machine learning. doi:10.1145/1015330.1015360