A Multivariate Unimodality Test Harnessing the Dip Statistic of Mahalanobis Distances Over Random Projections

Prodromos Kolyvakis; Aristidis Likas

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arXiv · 2311.16614

A Multivariate Unimodality Test Harnessing the Dip Statistic of Mahalanobis Distances Over Random Projections

Prodromos Kolyvakis·Aristidis Likas

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Abstract

Unimodality, pivotal in statistical analysis, offers insights into dataset structures and drives sophisticated analytical procedures. While unimodality's confirmation is straightforward for one-dimensional data using methods like Silverman's approach and Hartigans' dip statistic, its generalization to higher dimensions remains challenging. By extrapolating one-dimensional unimodality principles to multi-dimensional spaces through linear random projections and leveraging point-to-point distancing, our method, rooted in $α$-unimodality assumptions, presents a novel multivariate unimodality test named mud-pod. Both theoretical and empirical studies confirm the efficacy of our method in unimodality assessment of multidimensional datasets as well as in estimating the number of clusters.

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Prodromos Kolyvakis, Aristidis Likas. 2024-07-04. A Multivariate Unimodality Test Harnessing the Dip Statistic of Mahalanobis Distances Over Random Projections. https://arxiv.org/abs/2311.16614

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