Semi-supervised Distance Metric Learning in High-Dimensional Spaces by Using Equivalence Constraints

This paper introduces a semi-supervised distance metric learning algorithm which uses pairwise equivalence (similarity and dissimilarity) constraints to discover the desired groups within high-dimensional data. In contrast to the traditional full rank distance metric learning algorithms, the proposed method can learn nonsquare projection matrices that yield… (More)