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Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering
Shyam Narayanan · Sandeep Silwal · Piotr Indyk · Or Zamir

Thu Jul 22 07:40 AM -- 07:45 AM (PDT) @
in Semisupervised and Unsupervised Learning 1 »
Random dimensionality reduction is a versatile tool for speeding up algorithms for high-dimensional problems. We study its application to two clustering problems: the facility location problem, and the single-linkage hierarchical clustering problem, which is equivalent to computing the minimum spanning tree. We show that if we project the input pointset $X$ onto a random $d = O(d_X)$-dimensional subspace (where $d_X$ is the doubling dimension of $X$), then the optimum facility location cost in the projected space approximates the original cost up to a constant factor. We show an analogous statement for minimum spanning tree, but with the dimension $d$ having an extra $\log \log n$ term and the approximation factor being arbitrarily close to $1$. Furthermore, we extend these results to approximating {\em solutions} instead of just their {\em costs}. Lastly, we provide experimental results to validate the quality of solutions and the speedup due to the dimensionality reduction. Unlike several previous papers studying this approach in the context of $k$-means and $k$-medians, our dimension bound does not depend on the number of clusters but only on the intrinsic dimensionality of $X$.
Keywords: Dimensionality Reduction · Algorithms

Author Information

Shyam Narayanan (Massachusetts Institute of Technology)
Sandeep Silwal (MIT)
Piotr Indyk (MIT)
Or Zamir (Tel Aviv University)

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