Subject
2 entries
Kernel Methods
Bookmarks
Kernel PCA
Sebastian Raschka's tutorial on Kernel PCA — extending standard PCA to capture non-linear structure using the kernel trick with RBF kernels. Includes Python implementation, making it one of the clearest practical explanations of the technique available in 2014.
The Kernel Trick
Eric Kim's explanation of the kernel trick in support vector machines — how kernels enable SVMs to classify non-linearly separable data by implicitly mapping it to a higher-dimensional space. One of the cleaner intuitive explanations of a mathematically dense concept.