Fast and Provably Effective Multi-view Classification with Landmark-Based SVM

Abstract

We introduce a fast and theoretically founded method for learning landmark-based SVMs in a multi-view classification setting which leverages the complementary information of the different views and linearly scales with the size of the dataset. The proposed method-called MVL-SVM-applies a non-linear projection to the dataset through multi-view similarity estimates w.r.t. a small set of randomly selected landmarks, before learning a linear SVM in this latent space joining all the views. Using the uniform stability framework, we prove that our algorithm is robust to slight changes in the training set leading to a generalization bound depending on the number of views and landmarks. We also show that our method can be easily adapted to a missing-view scenario by only reconstructing the similarities to the landmarks. Empirical results, both in complete and missing view settings, highlight the superior performances of our method, in terms of accuracy and execution time, w.r.t. state of the art techniques.

Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting

“…To overcome the latter, some numerical approximation methods have been developed [23,10]. Landmarkbased approaches [4,3,5,27] can be used to reduce the number of instances to consider in order to reduce the number of comparisons [21], but they heavily depend on the choice of the kernel. Tuning the kernel is, however, difficult and represents another drawback to tackle.…”