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k-Means Clustering Is Matrix Factorization

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arxiv 1512.07548 v1 pith:QXL6WBYI submitted 2015-12-23 stat.ML

Christian Bauckhage This is my paper · ORCID
classification stat.ML
keywords matrixclusteringk-meansdatafactorizationapproximationconventionaldifference
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We show that the objective function of conventional k-means clustering can be expressed as the Frobenius norm of the difference of a data matrix and a low rank approximation of that data matrix. In short, we show that k-means clustering is a matrix factorization problem. These notes are meant as a reference and intended to provide a guided tour towards a result that is often mentioned but seldom made explicit in the literature.

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Cited by 5 Pith papers

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