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Dimensionality Reduction for $k$-means Clustering
Published 26 Jul 2020 in cs.LG and stat.ML | (2007.13185v1)
Abstract: We present a study on how to effectively reduce the dimensions of the $k$-means clustering problem, so that provably accurate approximations are obtained. Four algorithms are presented, two \textit{feature selection} and two \textit{feature extraction} based algorithms, all of which are randomized.
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