Bayesian clustering of high-dimensional data via latent repulsive mixtures
通过潜在排斥混合模型进行高维数据的贝叶斯聚类
Summary Model-based clustering of moderate- or large-dimensional data is notoriously difficult. We propose a model for simultaneous dimensionality reduction and clustering by assuming a mixture model for a set of latent scores, which are then linked to the observations via a Gaussian latent factor model. This approach was recently investigated by Chandra et al. (2023). The authors used a factor-analytic representation and assumed a mixture model for the latent factors.…