Abstract
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.
This work has been partially funded by FEDER and Spanish MEC through projects DPI2006-15542-C04-04 and Consolider Ingenio 2010 CSD2007-00018.
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Perez-Suay, A., Ferri, F.J. (2008). Scaling Up a Metric Learning Algorithm for Image Recognition and Representation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_58
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DOI: https://doi.org/10.1007/978-3-540-89646-3_58
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