Data Science, Kernel Trick, Nonlinear Models, Machine Learning, Data Transformation.
unsupervised (k-means, PCA), which can be kernelized to map problems into a higher dimensional kernel-space. This can help to solve more complex problems but brings the dependency of the kernel matrix, which scales quadratically with the number of given samples. For problems with a bigger number of