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I am searching for some kind of transform if it is possible, similar to a fourier transform, but for an arbitrary function.

Sort of an inverse convolution but with a kernel that varies in each point.

Or, like I say in the title of this topic a sort of continuous equivalent of fitting a curve to a sum of functions.

For example if I want to use Gaussians, I want to reproduce a function [tex] F(x) [/tex]

As:

[tex] F(x) = \int \frac{f(y)}{\sqrt{4\pi t(y)}}e^{-\frac{(x-y)^2}{4 t(y)}} dy [/tex]

Notice how t is a function of y.

This is easy for a finite sum of Gaussians with linear regression, but I'm searching for a continuous equivalent.

The closest thing that I found for Gausses is a Weierstrass transform. But the 'standard deviation' of the gausses doesn't vary in each point.

There are a ton of subjects that come close (linear regression, inverse convolution, Weierstrass transform,..) but they either are discrete or lack the variability of the convoluting kernel.

Does someone know a mathematical technique that can do this? Or know in what direction I have to look?

Thanks!