\textbf{Motivation:} Regression using feature maps $\phi: \R^d \to \R^p$:
$$
    \underset{w \in \R^p}{\min}\frac{1}{n}\sum_{i=1}^{n}l\Bigl( y_i, w^\top \cdot \phi(x_i) \Bigr)
$$
What if computing/storing $\phi(x)$ is expensive/infeasible?\\
\subtext{e.g. if $p$ is large, or infinite}

{\scriptsize
    \remark To store a poly. $p(x): \R^d \to \R$ with $\deg(p)=m$ we require $p=\mathcal{O}(d^m)$ features. Storing $n$ data points requires $\mathcal{O}(nd^m)$ memory. Not good.
}

\subsection{Kernelization}

By constraining $w$ to $\text{span}(\Phi^\top) \subset \R^p$ we can drastically improve memory usage. Since we know a minimizer exists here, we don't "lose anything".

\definition \textbf{Kernelization}

\begin{enumerate}
    \item \textbf{Reparametrization}: We assume $w = \Phi^\top\alpha$ (i)
    \item \textbf{Loss via Inner Products}: Observe:
    $$
        f(x) = w^\top \phi(x) \overset{\text{(i)}}{=} (\Phi^\top \alpha)^\top \phi(x) = \sum_{i=1}^{n} \alpha_i \Bigl( \phi(x_i)^\top \phi(x) \Bigr)
    $$
    Note: $x_i$ only appears in \textit{inner products} $\phi(x_i)^\top \phi(x_j)$
    \item \textbf{Replace Inner Products}: We define:
    $$
        k:\begin{cases}
            \R^d\times\R^d\to\R \\
            k(x,x') = \phi(x)^\top \phi(x')
        \end{cases}
        \quad
        K:\begin{cases}
            K \in \R^{n\times n} \\
            K_{ij} = k(x_i,x_j)
        \end{cases}
    $$
\end{enumerate}

Now, we can reformulate the optimization problem:
$$
    \underset{\alpha\in\R^n}{\min}\frac{1}{n}\sum_{i=1}^{n}l\Biggl( y_i, \sum_{j=1}^{n}\alpha_j k(x_i,x_j) \Biggr) = \underset{\alpha\in\R^n}{\min}\frac{1}{n}\sum_{i=1}^{n}l\Bigl( y_i, (K\alpha)_i \Bigr)
$$

By storing $K \in \R^{n\times n}$ instead of $\phi(x) \in \R^p$ for $i=1,\ldots,n$, the memory usage is reduced: $\mathcal{O}(np) \to \mathcal{O}(n^2)$.

\newpage
\subsection{The Kernel Trick}

Using $k$, the computation time is still $\mathcal{O}(n^2p)$ if 
$$
    k(x_i,x_j) = \phi(x_i)^\top \phi(x_j)
$$
So let's replace $k$ with a simple function, which guarantees the existance of some $\phi$ (which we never calculate).

{\scriptsize
    \remark Since we only \textit{implicitly} specify $\phi$ via $k$, we can use $\phi$ s.t. $p=\infty$ now.
}

\definition \textbf{Kernel Function} $k: \R^d \times \R^d \to \R$
\begin{enumerate}
    \item $k$ is symmetric: $\forall x,x':\ k(x,x') = k(x',x)$
    \item $k$ is PSD: $\forall n \in \N, \forall (x_1,\ldots,x_n) \in \R^d$:
    $$
        K = \begin{bmatrix}
            k(x_1,x_1)  & \cdots    & k(x_1,x_n)    \\
            \vdots      & \ddots    & \vdots        \\
            k(x_n,x_1)  & \cdots    & k(x_n,x_n)
        \end{bmatrix} \text{ is PSD}
    $$
\end{enumerate}

\theorem \textbf{Kernels guarantee existance of} $\phi$\\
\smalltext{If $k$ is a kernel, there exists a Hilbert Space $\Bigl(\mathcal{H},\langle\cdot,\cdot\rangle_\mathcal{H}\Bigr)$ s.t.}
$$
    \exists\phi:\R^d\to\mathcal{H} \text{ s.t. } k(x.x') = \Bigl\langle \phi(x),\phi(x') \Bigr\rangle_\mathcal{H} \forall x,x' \in \R^d
$$
\subtext{$\mathcal{H}$ may be, for example, $\R^p$ with $\Vert\cdot\Vert_2$.}

\lemma \textbf{Properties of Kernels}
\begin{enumerate}
    \item Composed feature maps are Kernels
    $$
        \begin{rcases*}
            \phi: \R^d \to \R^p \\
            \psi: \R^d \to \R^p
        \end{rcases*}
        \quad k(x,x') = \Bigl\langle \psi\bigl( \phi(x) \bigr), \psi\bigl( \phi(x') \bigr) \Bigr\rangle
    $$
    \item Kernels can be added in 2 ways, yielding a kernel
    \begin{align*}
        \text{(i)}\quad     & k\Bigl( (x,y),(x',y') \Bigr)    &= k_1(x,x') + k_2(y,y') \\
        \text{(ii)}\quad    & k(x,x')                         &= k_1(x,x') + k_2(x,x')  
    \end{align*}
    \item Kernels can be multiplied in 2 ways, yielding a kernel
\end{enumerate}

\lemma \textbf{Non-negative Taylor Series}\\
\smalltext{$g:\R\to\R$ s.t. $g$ has only non-neg. coeffs. in its Taylor Series}
$$
    k(x,x') = g(\langle x,x' \rangle) \text{ is a valid kernel}
$$

% Add Polynomial, RBF (Gaussian, Laplacian)