Abstract
Distance metric learning has been a major research topic in recent times. Usually, the problem is formulated as finding a Mahalanobis-like metric matrix that satisfies a set of constraints as much as possible. Different ways to introduce these constraints and to effectively formulate and solve the optimization problem have been proposed. In this work, we start with one of these formulations that leads to a convex optimization problem and generalize it in order to increase the efficiency by appropriately selecting the set of constraints. Moreover, the original criterion is expressed in terms of a reduced set of representatives that is learnt together with the metric. This leads to further improvements not only in efficiency but also in the quality of the obtained metrics.
This work has been partially funded by FEDER and Spanish MEC through projects TIN2009-14205-C04-03, TIN2011-29221-C03-02 and Consolider Ingenio 2010 CSD2007-00018.
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Perez-Suay, A., Ferri, F.J., Arevalillo-Herráez, M., Albert, J.V. (2014). About Combining Metric Learning and Prototype Generation. In: Fränti, P., Brown, G., Loog, M., Escolano, F., Pelillo, M. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2014. Lecture Notes in Computer Science, vol 8621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44415-3_33
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DOI: https://doi.org/10.1007/978-3-662-44415-3_33
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