Function approximation and interpolation

June 9, 2016 — June 9, 2016

functional analysis
Hilbert space
signal processing
sparser than thou
Figure 1

On constructing an approximation of some arbitrary function, and measuring the badness thereof.

THIS IS CHAOS RIGHT NOW. I need to break out the sampling/interpolation problem for regular data, for one thing.

1 Choosing the best approximation

In what sense? Most compact? Most easy to code?

If we are not interpolating, how much smoothing do we do?

We can use cross-validation, especially so-called “generalized” cross validation, to choose smoothing parameter this efficiently, in some sense.

Or you might have noisy data, in which case you now have a function approximation and inference problem, with error due to both approximation and sampling complexity. Compressive sensing can provide finite-sample guarantees for some of these settings.

To discuss: loss functions.

An interesting problem is how you align the curves that are your objects of study; That is a problem of warping.

2 Spline smoothing of observations

The classic.

Special superpowers: Easy to differentiate and integrate.

Special weakness: many free parameters, not so easy to do in high dimension.

See splines.

3 Polynomial bases

See polynomial bases.

4 Fourier bases


5 Radial basis function approximation

I# Radial basis function approximation I actually care about this mostly for densities, so see mixture models, for what information I do have.

6 Rational approximation

Padé’s is the method I’ve heard of. Are there others? Easy to differentiate. OK to integrate if you cheat using a computational algebra package.

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