Maximally Collapsing Metric Learning is a recently proposed algorithm to estimate a metric matrix from labelled data. The purpose of this work is to extend this approach by considering a set of landmark points which can in principle reduce the cost per iteration in one order of magnitude. The proposal is in fact a generalized version of the original algorithm that can be applied to larger amounts of higher dimensional data. Exhaustive experimentation shows that very similar behavior at a lower cost is obtained for a wide range of the number of landmark points used. © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Perez-Suay, A., & Ferri, F. J. (2008). Scaling up a metric learning algorithm for image recognition and representation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5359 LNCS, pp. 592–601). https://doi.org/10.1007/978-3-540-89646-3_58
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