Abstract
A recently proposed metric learning algorithm which enforces the optimal discrimination of the different classes is extended and empirically assessed using different kinds of publicly available data. The optimization problem is posed in terms of landmark points and then, a stochastic approach is followed in order to bypass some of the problems of the original algorithm. According to the results, both computational burden and generalization ability are improved while absolute performance results remain almost unchanged.
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., Albert, J.V. (2009). A Random Extension for Discriminative Dimensionality Reduction and Metric Learning. In: Araujo, H., Mendonça, A.M., Pinho, A.J., Torres, M.I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2009. Lecture Notes in Computer Science, vol 5524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02172-5_48
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DOI: https://doi.org/10.1007/978-3-642-02172-5_48
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