High-dimensional clustering via Random Projections
Methodology
2020-11-24 v2
Abstract
In this work, we address the unsupervised classification issue by exploiting the general idea of Random Projection Ensemble. Specifically, we propose to generate a set of low dimensional independent random projections and to perform model-based clustering on each of them. The top projections, i.e. the projections which show the best grouping structure are then retained. The final partition is obtained by aggregating the clusters found in the projections via consensus. The performances of the method are assessed on both real and simulated datasets. The obtained results suggest that the proposal represents a promising tool for high-dimensional clustering.
Cite
@article{arxiv.1909.10832,
title = {High-dimensional clustering via Random Projections},
author = {Laura Anderlucci and Francesca Fortunato and Angela Montanari},
journal= {arXiv preprint arXiv:1909.10832},
year = {2020}
}