Adaptive Transfer Clustering: A Unified Framework
This work addresses clustering challenges in transfer learning for researchers and practitioners, offering a novel framework but with incremental improvements over existing methods.
The paper tackles the problem of clustering with auxiliary data by proposing an adaptive transfer clustering (ATC) algorithm that automatically leverages commonality while handling discrepancies, resulting in proven optimality and effectiveness in simulations and real data.
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.