Clustering of Nonnegative Data and an Application to Matrix Completion

TL;DR

This paper introduces a straightforward clustering algorithm for nonnegative data in disjoint subspaces and applies it to enhance matrix completion, outperforming standard methods under specific conditions.

Contribution

The paper presents a novel clustering algorithm for nonnegative data and demonstrates its application to improve matrix completion performance.

Findings

Clustering algorithm effectively groups nonnegative data in disjoint subspaces.

The matrix completion method outperforms standard algorithms under certain data conditions.

Performance depends on correlation measures between subspaces.

Abstract

In this paper, we propose a simple algorithm to cluster nonnegative data lying in disjoint subspaces. We analyze its performance in relation to a certain measure of correlation between said subspaces. We use our clustering algorithm to develop a matrix completion algorithm which can outperform standard matrix completion algorithms on data matrices satisfying certain natural conditions.