Function Approximation
35 Low-Dimensional Structure

Numerical Methods for Data Science

Preface
1 Introduction
Background Plus a Bit
Fundamentals in 1D
Numerical Linear Algebra
Nonlinear Equations and Optimization
Computing with Randomness
Dimension Reduction and Latent Factor Models
Function Approximation
Network Analysis
Learning Dynamics
References

Function Approximation
35 Low-Dimensional Structure

35 Low-Dimensional Structure

On input: active subspace, sloppy models, random embed for optimization On output: POD, DEIM, etc; approximation from a subspace

34 Fundamental Concepts

36 Kernels and RBFs