Multiscale Dictionary Learning: Non-Asymptotic Bounds and Robustness

TL;DR

This paper provides non-asymptotic probabilistic bounds for the Geometric Multi-Resolution Analysis (GMRA), demonstrating its robustness and efficiency in learning low-dimensional structures like manifolds and dictionaries in high-dimensional data.

Contribution

It establishes theoretical non-asymptotic bounds for GMRA's approximation error on noisy manifolds, confirming its robustness and independence from ambient dimension.

Findings

GMRA's approximation error is independent of ambient dimension.

Theoretical bounds confirm GMRA's robustness on noisy manifolds.

Numerical experiments support the theoretical guarantees.

Abstract

High-dimensional datasets are well-approximated by low-dimensional structures. Over the past decade, this empirical observation motivated the investigation of detection, measurement, and modeling techniques to exploit these low-dimensional intrinsic structures, yielding numerous implications for high-dimensional statistics, machine learning, and signal processing. Manifold learning (where the low-dimensional structure is a manifold) and dictionary learning (where the low-dimensional structure is the set of sparse linear combinations of vectors from a finite dictionary) are two prominent theoretical and computational frameworks in this area. Despite their ostensible distinction, the recently-introduced Geometric Multi-Resolution Analysis (GMRA) provides a robust, computationally efficient, multiscale procedure for simultaneously learning manifolds and dictionaries. In this work, we prove non-asymptotic probabilistic bounds on the approximation error of GMRA for a rich class of data-generating statistical models that includes "noisy" manifolds, thereby establishing the theoretical robustness of the procedure and confirming empirical observations. In particular, if a dataset aggregates near a low-dimensional manifold, our results show that the approximation error of the GMRA is completely independent of the ambient dimension. Our work therefore establishes GMRA as a provably fast algorithm for dictionary learning with approximation and sparsity guarantees. We include several numerical experiments confirming these theoretical results, and our theoretical framework provides new tools for assessing the behavior of manifold learning and dictionary learning procedures on a large class of interesting models.