The Internet Archive has a preservation copy of this work in our general collections.
The file type is
Approximating non-linear kernels using feature maps has gained a lot of interest in recent years due to applications in reducing training and testing times of SVM classifiers and other kernel based learning algorithms. We extend this line of work and present low distortion embeddings for dot product kernels into linear Euclidean spaces. We base our results on a classical result in harmonic analysis characterizing all dot product kernels and use it to define randomized feature maps into explicitarXiv:1201.6530v3 fatcat:xdpgqlyugvhs3py64apzdvxzmu