Abstract: The Kernel k-Means algorithm for clustering extends the classic k-Means clustering algorithm. It uses the kernel trick to implicitly calculate distances on a higher dimensional space, thus ...

Abstract: Kernel Principal Component Analysis (KPCA) is a nonlinear feature extraction approach, which generally needs to eigen-decompose the kernel matrix. But the size of kernel matrix scales with ...

This script tests the model’s accuracy and loss on the MNIST test set, validating whether the model works as expected after training with the custom kernel. While the custom CUDA kernel is used ...

CMATMUL is an optimised C++ implementation of matrix multiplication that leverages modern CPU features such as AVX2/FMA SIMD vectorisation and OpenMP multi-threading for increased throughput. This ...