Seonglae ChoThe Kernel Trick is different from Kernel lol. The strategy is to embed the data into a space where the patterns can be discovered as linear relations.
- Kernel function that maps the data into the embedding or feature space
- Learning algorithm designed to discover linear patterns in that space
The input data is mapped to a higher-dimensional space to enable modeling with linear functions, using inner product operations in the mapped space to calculate linear functions. By doing this, non-linear problems are solved. For Non-separable case, kernel mapping increase the likelihood to find linearly separable but cannot guarantee it.
The algorithms are implemented in such a way that the coordinates of the embedded points are not needed, only their pairwise inner products. The pairwise inner products can be computed efficiently directly from the original points using a kernel function. i.e. Theoretically Feature Map is required. However, calculations are possible by just knowing the Kernel Function. Gram Matrix is used with valid kernel functions.
