Adaptive Transfer Clustering: A Unified Framework

• 成果类型: Article; Early Access
• 署名作者: Gu, Yuqi; Lyu, Zhongyuan; Wang, Kaizheng
• 署名单位: Columbia University; Columbia University; University of Sydney
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2654222
• 发表日期: 2026-06-06
• 关键词: adaptation; bootstrap; multiview clustering; Transfer Learning; community detection; gaussian mixtures; em algorithm; networks; MODEL

摘要

We propose a general transfer learning framework for clustering given a main dataset and an auxiliary one about the same subjects. The two datasets may reflect similar but different latent grouping structures of the subjects. We propose an adaptive transfer clustering (ATC) algorithm that automatically leverages the commonality in the presence of unknown discrepancy, by optimizing an estimated bias-variance decomposition. It applies to a broad class of statistical models including Gaussian mixture models, stochastic block models, and latent class models. A theoretical analysis proves the optimality of ATC under the Gaussian mixture model and explicitly quantifies the benefit of transfer. Extensive simulations and real data experiments confirm our method's effectiveness in various scenarios. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.