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

Léo Gautheron (1) , Pascal Germain (2) , Amaury Habrard (1) , Emilie Morvant (1) , Marc Sebban (1) , Valentina Zantedeschi (1)

1 LHC - Laboratoire Hubert Curien
2 MODAL - MOdel for Data Analysis and Learning

We propose a Gradient Boosting algorithm for learning an ensemble of kernel functions adapted to the task at hand. Unlike state-of-the-art Multiple Kernel Learning techniques that make use of a pre-computed dictionary of kernel functions to select from, at each iteration we fit a kernel by approximating it as a weighted sum of Random Fourier Features (RFF) and by optimizing their barycenter. This allows us to obtain a more versatile method, easier to setup and likely to have better performance. Our study builds on a recent result showing one can learn a kernel from RFF by computing the minimum of a PAC-Bayesian bound on the kernel alignment generalization loss, which is obtained efficiently from a closed-form solution. We conduct an experimental analysis to highlight the advantages of our method w.r.t. both Boosting-based and kernel-learning state-of-the-art methods.

Domaines: Machine Learning [stat.ML]

Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, et al.. Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting. 2019. ⟨hal-02148618⟩