Learning Random Kernel Approximations for Object Recognition

TL;DR

This paper introduces a method for learning random Fourier feature approximations to improve large-scale kernel-based object recognition, optimizing kernel parameters in the Fourier domain for better prediction accuracy.

Contribution

It develops a linear Fourier approximation approach for single and multiple kernel learning, enhancing efficiency and accuracy in object recognition tasks.

Findings

Fast and accurate predictors on VOC2011 dataset

Efficient optimization of kernel parameters in Fourier domain

Scalable reformulation of multiple kernel learning

Abstract

Approximations based on random Fourier features have recently emerged as an efficient and formally consistent methodology to design large-scale kernel machines. By expressing the kernel as a Fourier expansion, features are generated based on a finite set of random basis projections, sampled from the Fourier transform of the kernel, with inner products that are Monte Carlo approximations of the original kernel. Based on the observation that different kernel-induced Fourier sampling distributions correspond to different kernel parameters, we show that an optimization process in the Fourier domain can be used to identify the different frequency bands that are useful for prediction on training data. Moreover, the application of group Lasso to random feature vectors corresponding to a linear combination of multiple kernels, leads to efficient and scalable reformulations of the standard multiple kernel learning model \cite{Varma09}. In this paper we develop the linear Fourier approximation methodology for both single and multiple gradient-based kernel learning and show that it produces fast and accurate predictors on a complex dataset such as the Visual Object Challenge 2011 (VOC2011).