# A Data-Informed Variational Clustering Framework for Noisy High-Dimensional Data Source: [A Data-Informed Variational Clustering Framework for Noisy High-Dimensional Data]() Collection index: [llms.txt]() Data updated: 2026-04-10T05:00:04.898Z Authors: Wan Ping Chen Published: 2026-04-08T09:25:44.000Z ## Abstract 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. We propose DIVI, a data-informed variational clustering framework that combines global feature gating with split-based adaptive structure growth. DIVI uses informative prior initialization to stabilize optimization, learns feature relevance in a differentiable manner, and expands model complexity only when local diagnostics indicate underfit. Beyond clustering performance, we also examine runtime scalability and parameter sensitivity in order to clarify the computational and practical behavior of the framework. Empirically, we find that DIVI performs competitively under severe feature noise, remains computationally feasible, and yields interpretable feature-gating behavior, while also exhibiting conservative growth and identifiable failure regimes in challenging settings. Overall, DIVI is best viewed as a practical variational clustering framework for noisy high-dimensional data rather than as a fully Bayesian generative solution. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Clustering on 20NG](): Clustering; 20NG; ARI: 0.759. - [Clustering on Matched Synthetic Benchmark N=200 D=100, 90% irrelevant dimensions (Small-n)](): Clustering; Matched Synthetic Benchmark N=200 D=100, 90% irrelevant dimensions (Small-n); Number of Clusters (K): 3. - [Clustering on Matched Synthetic Benchmark N=1000 D=100 90% irrelevant dimensions (Large-n)](): Clustering; Matched Synthetic Benchmark N=1000 D=100 90% irrelevant dimensions (Large-n); K: 3. - [Clustering on ISOLET (ARI, NMI)](): Clustering; Isolet; ARI: 0.46. ## Original sources and implementation [Original paper]() [PDF]()