非负数据的聚类及其在矩阵补全中的应用

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2020-05-01 DOI:10.1109/ICASSP40776.2020.9052980

Christopher Strohmeier, D. Needell

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引用次数: 0

摘要

在本文中，我们提出了一个简单的算法来聚类位于不相交子空间中的非负数据。我们根据所述子空间之间的某种相关度量来分析其性能。我们利用我们的聚类算法开发了一种矩阵补全算法，该算法在满足一定自然低秩条件的数据矩阵上优于标准矩阵补全算法。

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Clustering of Nonnegative Data and an Application to Matrix Completion

In this article, 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 a certain natural low rank condition.

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ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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