Unsupervised Feature Selection for the k-means Clustering Problem
by
Christos Boutsidis
,
Michael W. Mahoney
,
Petros Drineas
| Citations: | 2 - 1 self |
BibTeX
@MISC{Boutsidis_unsupervisedfeature,
author = {Christos Boutsidis and Michael W. Mahoney and Petros Drineas},
title = {Unsupervised Feature Selection for the k-means Clustering Problem},
year = {}
}
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Abstract
We present a novel feature selection algorithm for the k-means clustering problem. Our algorithm is randomized and, assuming an accuracy parameter ϵ ∈ (0, 1), selects and appropriately rescales in an unsupervised manner Θ(k log(k/ϵ)/ϵ 2) features from a dataset of arbitrary dimensions. We prove that, if we run any γ-approximate k-means algorithm (γ ≥ 1) on the features selected using our method, we can find a (1 + (1 + ϵ)γ)-approximate partition with high probability. 1
