For , a polynomial kernel is the positive-semidefinite kernel , where is a positive integer and . The case uses monomials of total degree ; also includes lower degrees. The feature map weights matter: listing each distinct monomial once with coefficient one generally gives a different inner product.[ref]
Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Christopher K. I. Williams. Section 4.2.2, pp. 89–90, equations (4.23)–(4.24): polynomial kernels and weighted monomial features
For the homogeneous quadratic case in two dimensions, take the scaled quadratic feature mapThe inner product identity is explicit:The factor accounts for the two equal cross terms in the square. Thus an inner product of three derived coordinates can be obtained from an inner product of the two input coordinates followed by squaring. The kernel trick uses this equality inside an algorithm; the feature map explains which nonlinear functions the algorithm can represent.
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