DeepAI AI Chat
Log In Sign Up

Regularized K-means through hard-thresholding

by   Jakob Raymaekers, et al.

We study a framework of regularized K-means methods based on direct penalization of the size of the cluster centers. Different penalization strategies are considered and compared through simulation and theoretical analysis. Based on the results, we propose HT K-means, which uses an ℓ_0 penalty to induce sparsity in the variables. Different techniques for selecting the tuning parameter are discussed and compared. The proposed method stacks up favorably with the most popular regularized K-means methods in an extensive simulation study. Finally, HT K-means is applied to several real data examples. Graphical displays are presented and used in these examples to gain more insight into the datasets.


page 22

page 24

page 27


Biclustering Methods via Sparse Penalty

In this paper, we first reviewed several biclustering methods that are u...

Iterative Hard Thresholding Methods for l_0 Regularized Convex Cone Programming

In this paper we consider l_0 regularized convex cone programming proble...

Robust Clustering Using Tau-Scales

K means is a popular non-parametric clustering procedure introduced by S...

On Uses of Van der Waerden Test: A Graphical Approach

Although several nonparametric tests are available for testing populatio...

Deep clustering: On the link between discriminative models and K-means

In the context of recent deep clustering studies, discriminative models ...

Variable Clustering via Distributionally Robust Nodewise Regression

We study a multi-factor block model for variable clustering and connect ...