Kernel Trick

CORE DEFINITION

A technique that maps data from the original feature space to a high-dimensional feature space, making problems that are non-linearly separable in the original space become linearly separable in the high-dimensional space. By implicitly computing the inner product in the high-dimensional space through a kernel function, it avoids explicit high-dimensional mapping computations. Scaffolding role: non-linear pattern recognition. It provides a mathematical foundation for algorithms such as support vector machines to handle non-linear problems, enabling machine learning to discover complex patterns in data, and is of key significance to the development of modern artificial intelligence.