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== Moore-Aronszajn theorem ==
Given a [[positive definite kernel]]
Define the operator <math>T_K : L_2 (X) \rightarrow L_2 (X)</math> by
:<math> (T_K f) (x) = \int_X \overline {K (x, y)} f(y)\,dy </math>
or, equivalently, <math> (T_K f)(x) = \langle K_x, f \rangle_2 </math>
(where <math>\langle \cdot, \cdot \rangle_2 </math> denotes the standard inner product on ''L''<sub>2</sub>''(X)'').
Let ''H'' be the image of ''L''<sub>2</sub> ''(X)'' under ''T''<sub>''K''</sub> and define an inner product on ''H'' by
:<math> \langle f, g \rangle_H \ \stackrel{\mathrm{def}}{=} \ \langle f, T_K^{-1} g \rangle_2. </math>
Note that ''T''<sub>''K''</sub><sup>-1</sup> is self-adjoint (we will write down exactly what it is later) and so
<math> \langle f, T_K^{-1} g \rangle_2 = \langle T_K^{-1} f, g \rangle_2 </math>. It is now easy to check that this defines a reproducing kernel Hilbert space. Indeed,
:<math>
\langle K_x, f \rangle_H = \langle K_x, T_K^{-1} f \rangle_2
= (T_K (T_K^{-1} f)) (x) = f(x)
</math>
as required.
[[Mercer's theorem]] gives us another way to represent ''H''. Let {λ<sub>''i''</sub>} be a sequence of eigenvalues of ''T''<sub>''K''</sub> and let {''e''<sub>''i''</sub>} be the corresponding eigenvectors. Then we can write the operator ''T''<sub>''K''</sub> as
:<math> T_K \left( \sum_i a_i e_i \right) = \sum_i \lambda_i a_i e_i
</math>
and we can write the inner product on ''H'' as
:<math> \left \langle \sum_i a_i e_i, \sum_i b_i e_i \right \rangle_H = \sum_i \frac {a_i b_i}{\lambda_i} </math>.
== See also ==
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