Learning Non-Parametric Basis Independent Models from Point Queries via Low-Rank Methods
This work addresses function approximation in high-dimensional spaces for applications like data analysis, but it is incremental as it builds on low-rank matrix recovery techniques.
The paper tackles the problem of learning multi-ridge functions from point queries by proposing a randomized sampling scheme with polynomial complexity, achieving uniform approximation guarantees and noise robustness as demonstrated in numerical examples.
We consider the problem of learning multi-ridge functions of the form f(x) = g(Ax) from point evaluations of f. We assume that the function f is defined on an l_2-ball in R^d, g is twice continuously differentiable almost everywhere, and A \in R^{k \times d} is a rank k matrix, where k << d. We propose a randomized, polynomial-complexity sampling scheme for estimating such functions. Our theoretical developments leverage recent techniques from low rank matrix recovery, which enables us to derive a polynomial time estimator of the function f along with uniform approximation guarantees. We prove that our scheme can also be applied for learning functions of the form: f(x) = \sum_{i=1}^{k} g_i(a_i^T x), provided f satisfies certain smoothness conditions in a neighborhood around the origin. We also characterize the noise robustness of the scheme. Finally, we present numerical examples to illustrate the theoretical bounds in action.