Fast Food: Approximating Kernel Expansion in Loglinear Time
published: Jan. 18, 2013, recorded: December 2012, views: 1121
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The ability to evaluate nonlinear function classes rapidly is crucial for nonparametric estimation. We propose an improvement to random kitchen sinks that offers O(n log d) computation and O(n) storage for n basis functions in d dimensions without sacrificing accuracy. We show how one may adjust the regularization properties of the kernel simply by changing the spectral distribution of the projection matrix. Experiments show that we achieve identical accuracy to full kernel expansions and random kitchen sinks 100x faster with 1000x less memory.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !