Representer theorem for a loss / functional of the form $L(h) := \sum_{i=1}^n (|h(x_i)-y_i|+t\|h\|)^2$ Let $K:X \times X \to \mathbb R$ be a (positive-definite) kernel and let $H$ be the induced reproducing kernel Hilbert space (RKHS). Fix $(x_1,y_1),\ldots,(x_n,y_n) \in X \times \mathbb R$. For $t \ge 0$, consider the functional $L:H \to \mathbb R$ defined by
$$
\tag{1}
L(h) := \sum_{i=1}^n (|h(x_i)-y_i|+t\|h\|)^2.
$$

Question. Is there a representer theorem for minimizers of $L$? That is, is it true that every minimizer $h$ of $L$ can be written as $h = \sum_{i=1}^n c_i K_{x_i}$, where the $c_i$'s are scalars and $K_{x_i} \in H$ is defined by $K_{x_i}(x) = K(x_i,x)$  for any $x \in X$?

Note that if $t=0$, then $L = \sum_{i=1}^n (h(x_i)-y_i)^2$, and the classical representer theorem applies.

More generally, given functions $G:\mathbb R^{n+1} \to \mathbb R$ and $u:\mathbb R \to \mathbb R$, I'm interested in conditions under which the functional $L:H \to \mathbb R$ defined by
$$
L(h) := G(u(x_1,y_1),\ldots,u(x_n,y_n),\|h\|),
$$
admits a representer theorem. In particular, (1) corresponds to the case where $G(r_1,\ldots,r_n,b) = \sum_i^n (r_i + t b)^2$ and $u(c,d) = |c-d|$, for all $r_1,\ldots,r_n,b,c,d \in \mathbb R$.

A perhaps useful observation
For any $\alpha \in (0,1)$, define the function $L_{\alpha}:H \to \mathbb R$ by
$$
L_{\alpha}(h) := \dfrac{1}{\alpha}\sum_{i=1}^n(h(x_i)-y_i)^2 + \dfrac{t^2}{1-\alpha}\|h\|^2.
$$
Then, for the function $L:H \to \mathbb R$ defined in (1), one has the identity
$$
L(h) = \inf_{\alpha \in (0,1)}L_{\alpha}(h).
$$
It is clear that the $L_{\alpha}$'s admit a representer theorem. I don't know if any of this is useful.
 A: Let $h=h_1 + h_2$, with $h_1\in\mathrm{span}(\{K(\cdot, x_i)\}_{i=1}^n)$ and $h_2$ in the orthogonal complement of this last set. It follows that $\|h\|_H^2 = \|h_1\|_H^2+ \|h_2\|_H^2$, and $\|h\|_H^2 \geq \|h_1\|_H^2$.
The reproducing property, gives you the equality
$$h(x_i) = \langle h,K(\cdot,x_i)\rangle = \langle h_1,K(\cdot,x_i)\rangle + \langle h_2,K(\cdot,x_i)\rangle = \langle h_1,K(\cdot,x_i)\rangle =h_1(x_i)$$
Because $h_2$ is orthogonal to $\mathrm{span}(\{K(\cdot, x_i)\}_{i=1}^n)$.
It follows that, if $\|h_2\|>0$ then
$$
L(h) = \sum_{i=1}^n (|h(x_i)-y_i|+t\|h\|)^2>\sum_{i=1}^n (|h_1(x_i)-y_i|+t\|h_1\|)^2=L(h_1).$$
Remark:

*

*To a more general case, let $$L(h) := G(u(h(x_1),y_1),\ldots,u(h(x_n),y_n),\|h\|),$$ being an increasing function, with respect to $\|h\|$. It holds the inequality
$$L(h)\geq L(h_1),$$ and all minimizers of $L(h)$, if they exists, are in $\mathrm{span}(\{K(\cdot, x_i)\}_{i=1}^n)$.


*Searching for "\(\langle f,K(x,\cdot)\rangle\)" on SearchOnMath perhaps you find some usefull results about the representer theorem.
