Cluster analysis

Sparse Dual of the Density Peaks Algorithm for Cluster Analysis of High-Dimensional Data

The density peaks (DP) algorithm for cluster analysis, introduced by Rodriguez and Laio in 2014, has proven empirically competitive or superior in multiple aspects to other contemporary clustering algorithms. Yet, it suffers from certain drawbacks and limitations when used for clustering high-dimensional data. We introduce SD-DP, the sparse dual version of DP. While following the DP principle and maintaining its appealing properties, we establish a sparse descriptor of local density as a robust representation.