Clustering in statistical ill-posed linear inverse problems

TL;DR

This paper investigates how clustering affects the accuracy of solutions in ill-posed linear inverse problems, proposing a minimax optimal estimator that does not require prior knowledge of the number of clusters.

Contribution

It introduces a new estimator for grouped functions in ill-posed inverse problems, with theoretical guarantees and practical insights on clustering's impact.

Findings

Clustering improves estimation accuracy in moderately ill-posed problems.

The proposed estimator is nearly minimax optimal without knowing the number of clusters.

Clustering can be detrimental in severely ill-posed problems.

Abstract

In many statistical linear inverse problems, one needs to recover classes of similar curves from their noisy images under an operator that does not have a bounded inverse. Problems of this kind appear in many areas of application. Routinely, in such problems clustering is carried out at the pre-processing step and then the inverse problem is solved for each of the cluster averages separately. As a result, the errors of the procedures are usually examined for the estimation step only. The objective of this paper is to examine, both theoretically and via simulations, the effect of clustering on the accuracy of the solutions of general ill-posed linear inverse problems. In particular, we assume that one observes $X_m = A f_m + \delta \epsilon_m$, $m=1, \cdots, M$, where functions $f_m$ can be grouped into $K$ classes and one needs to recover a vector function ${\bf f}= (f_1,\cdots, f_M)^T$. We construct an estimators for ${\bf f}$ as a solution of a penalized optimization problem and derive an oracle inequality for its precision. By deriving upper and minimax lower bounds for the error, we confirm that the estimator is minimax optimal or nearly minimax optimal up to a logarithmic factor of the number of observations. One of the advantages of our estimation procedure is that we do not assume that the number of clusters is known in advance. We conclude that clustering at the pre-processing step is beneficial when the problem is moderately ill-posed. It should be applied with extreme care when the problem is severely ill-posed.