A method is described for learning a distance metric for use in object identification that does not require human supervision. It is based on two assumptions. One is that pairs of different names refer to different objects. The other is that names are arbitrary. These two assumptions justify using pairs of data items for objects with different names as "cannotbe-linked" example pairs for learning a distance metric for use in clustering ambiguous names. The metric learning is formulated using only dissimilar example pairs as a convex quadratic programming problem that can be solved much faster than a semi-definite programming problem, which generally must be solved to learn a distance metric matrix. Experiments on author identification using a bibliographic database showed that the learned metric improves identification F-measure. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Oyama, S., & Tanaka, K. (2006). Learning a distance metric for object identification without human supervision. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4213 LNAI, pp. 609–616). Springer Verlag. https://doi.org/10.1007/11871637_62
Mendeley helps you to discover research relevant for your work.