We propose Spherical Structured Feature (SSF) maps to approximate shift and rotation invariant kernels as well as bth-order arc-cosine kernels (Cho \& Saul, ...

We propose Spherical Structured Feature (SSF) maps to approximate shift and rotation invariant kernels as well as bth-order arc-cosine kernels (Cho & Saul,.

We propose Spherical Structured Feature (SSF) maps to approximate shift and rotation invariant kernels as well as b^{th}-order arc-cosine kernels (Cho ...

Duration: 12:15
Posted: Oct 31, 2017

Apr 3, 2019 · Bibliographic details on Spherical Structured Feature Maps for Kernel Approximation.

We consider the problem of improving kernel approximation via randomized feature maps. These maps arise as Monte Carlo approximation to integral.

This repository contains PyTorch implementations of various random feature maps for polynomial and general dot product kernels.

Spherical structured feature maps for kernel approximation. In International Conference on Machine Learning (ICML), 2017. Black-box Optimization and RL ...

Spherical structured feature maps for kernel approximation. Y Lyu. International Conference on Machine Learning (ICML), 2017. 28, 2017. Incremental hashing for ...

Compact explicit feature maps provide a practical framework to scale kernel methods to large-scale learning, but deriving such maps for many types of kernels ...