Clustering Of Nonnegative Data And An Application To Matrix Completion
Christopher Strohmeier, Deanna Needell
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04 May 2020
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.
