MLFeb 23, 2016
A Simple Approach to Sparse Clustering
arXiv:1602.07277v229 citations
AI Analysis
This is an incremental improvement for researchers in clustering and feature selection.
The paper tackled the problem of sparse clustering by introducing a simpler hill-climbing approach, showing it to be competitive with existing methods like COSA and Sparse K-means.
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.