Clustering of Nonnegative Data and an Application to Matrix Completion
This work addresses clustering and matrix completion problems for nonnegative data, offering incremental improvements in performance for applications like data analysis and recommendation systems.
The paper tackles clustering of nonnegative data in disjoint subspaces by proposing a simple algorithm and analyzes its performance based on subspace correlation measures, then applies it to matrix completion to outperform standard algorithms under specific conditions.
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.