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Clustering of Nonnegative Data and an Application to Matrix Completion

09/02/2020

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by C. Strohmeier, et al.

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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.

C. Strohmeier
1 publication
D. Needell
3 publications

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