Two-Manifold Problems

TL;DR

This paper introduces two-manifold algorithms that jointly reconstruct related manifolds from different data views, improving robustness to noise and bias reduction through spectral methods in Hilbert space.

Contribution

It proposes a novel class of spectral algorithms for two-manifold problems, enabling noise suppression and bias reduction by leveraging interconnected data views.

Findings

Algorithms successfully reconstruct manifolds from noisy data.

Joint learning improves robustness compared to single-view methods.

Application to nonlinear dynamical systems from limited data.

Abstract

Recently, there has been much interest in spectral approaches to learning manifolds---so-called kernel eigenmap methods. These methods have had some successes, but their applicability is limited because they are not robust to noise. To address this limitation, we look at two-manifold problems, in which we simultaneously reconstruct two related manifolds, each representing a different view of the same data. By solving these interconnected learning problems together and allowing information to flow between them, two-manifold algorithms are able to succeed where a non-integrated approach would fail: each view allows us to suppress noise in the other, reducing bias in the same way that an instrumental variable allows us to remove bias in a {linear} dimensionality reduction problem. We propose a class of algorithms for two-manifold problems, based on spectral decomposition of cross-covariance operators in Hilbert space. Finally, we discuss situations where two-manifold problems are useful, and demonstrate that solving a two-manifold problem can aid in learning a nonlinear dynamical system from limited data.