Support Vector Machine Framework for Multi-View Metric Learning

Abstract : In this article we tackle the supervised multi-view learning problem with kernel methods and metric learning. In this context we consider a recently developed multi-view metric learning (MVML) framework, and propose a SVM-based algorithm that jointly learns the classifier and metrics between views. These metrics permit taking into account the multi-view characteristics of the learning problem. Experiments on real data were performed to evaluate the performance of the proposed algorithm.
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https://hal-amu.archives-ouvertes.fr/hal-02070699
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Riikka Huusari, Hachem Kadri, Cécile Capponi. Support Vector Machine Framework for Multi-View Metric Learning. 50e Journées de Statistique de la Société Française de Statistique, May 2018, Paris, France. ⟨hal-02070699⟩

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