论文标题
$ k $ -Means群集的尺寸降低
Dimensionality Reduction for $k$-means Clustering
论文作者
论文摘要
我们介绍了一项有关如何有效降低$ k $ - 均值聚类问题的尺寸的研究，从而获得了准确的近似值。提出了四种算法，两种\ textit {功能选择}和两个基于TextIt {功能提取}的算法，所有这些都是随机的。
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.
