Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6868919 | Computational Statistics & Data Analysis | 2017 | 12 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Ery Arias-Castro, Xiao Pu,