A Data-Informed Variational Clustering Framework for Noisy High-Dimensional Data

ArXi:2604.06864v1 Announce Type: cross Clustering in high-dimensional settings with severe feature noise remains challenging, especially when only a small subset of dimensions is informative and the final number of clusters is not specified in advance. In such regimes, partition recovery, feature relevance learning, and structural adaptation are tightly coupled, and standard likelihood-based methods can become unstable or overly sensitive to noisy dimensions.