A Simple Approach to Sparse Clustering
Machine Learning
2017-03-01 v2
Abstract
Consider the problem of sparse clustering, where it is assumed that only a subset of the features are useful for clustering purposes. In the framework of the COSA method of Friedman and Meulman, subsequently improved in the form of the Sparse K-means method of Witten and Tibshirani, a natural and simpler hill-climbing approach is introduced. The new method is shown to be competitive with these two methods and others.
Keywords
Cite
@article{arxiv.1602.07277,
title = {A Simple Approach to Sparse Clustering},
author = {Ery Arias-Castro and Xiao Pu},
journal= {arXiv preprint arXiv:1602.07277},
year = {2017}
}