Deterministic Feature Selection for $k$-means Clustering

Christos Boutsidis; Malik Magdon-Ismail

doi:10.1109/TIT.2013.2255021

Deterministic Feature Selection for $k$-means Clustering

Machine Learning 2016-11-17 v4 Data Structures and Algorithms

Authors: Christos Boutsidis , Malik Magdon-Ismail

View on arXiv ↗ PDF ↗ DOI ↗

Abstract

We study feature selection for $k$ -means clustering. Although the literature contains many methods with good empirical performance, algorithms with provable theoretical behavior have only recently been developed. Unfortunately, these algorithms are randomized and fail with, say, a constant probability. We address this issue by presenting a deterministic feature selection algorithm for k-means with theoretical guarantees. At the heart of our algorithm lies a deterministic method for decompositions of the identity.

Keywords

cluster analysis feature selection algorithm selection

Cite

@article{arxiv.1109.5664,
  title  = {Deterministic Feature Selection for $k$-means Clustering},
  author = {Christos Boutsidis and Malik Magdon-Ismail},
  journal= {arXiv preprint arXiv:1109.5664},
  year   = {2016}
}