Efficient Kernel Machines Using the Improved Fast Gauss Transform

The computation and memory required for kernel machines with N training samples is at least O(N2 ). Such a complexity is significant even for moderate size problems and is prohibitive for large datasets. We present an approximation technique based on the improved fast Gauss transform to reduce the computation to O(N). We also give an error bound for the approximation, and provide experimental results on the UCI datasets.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Changjiang Yang, Ramani Duraiswami, Larry S. Davis
• Keywords: Fast Gauss Transform, Large Datasets, Moderate Size Problems, NIPS 2004, NIPS 2007