# Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering

8 Nov 2018Konstantin MakarychevYury MakarychevIlya Razenshteyn

Consider an instance of Euclidean $k$-means or $k$-medians clustering. We show that the cost of the optimal solution is preserved up to a factor of $(1+\varepsilon)$ under a projection onto a random $O(\log(k / \varepsilon) / \varepsilon^2)$-dimensional subspace... (read more)

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