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New distributional clustering method introduced using Wasserstein kernel

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Researchers have introduced the distributional determinantal point process (dDPP), a novel method for clustering probability distributions. This dDPP utilizes a sliced Wasserstein kernel and is demonstrated to be a valid point process. The framework allows for a distribution-valued random partition model with a repulsive generalized Bayesian mixture model, placing a dDPP prior over the mixing measure's atoms. The proposed method has been applied to single-cell gene expression data and human epilepsy data, yielding interpretable and well-separated clusters. AI

RANK_REASON The cluster contains a research paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

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New distributional clustering method introduced using Wasserstein kernel

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The cluster contains a research paper detailing a new statistical methodology. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Khai Nguyen, Yang Ni, Elizabeth Juarez-Colunga, Peter Mueller ·

    Distributional Determinantal Point Process for Repulsive Clustering of Distributions

    arXiv:2607.21847v1 Announce Type: cross Abstract: We introduce the distributional determinantal point process (dDPP) as a novel repulsive point process whose atoms are probability distributions rather than points in a real space. The dDPP is constructed via an L-ensemble with a s…

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