Adaptive Transfer Clustering: A Unified Framework

TL;DR

This paper introduces an adaptive transfer clustering framework that leverages auxiliary data to improve clustering accuracy across various statistical models, with theoretical guarantees and empirical validation.

Contribution

It presents a novel adaptive transfer clustering algorithm that automatically exploits shared structure despite unknown discrepancies, applicable to multiple statistical models.

Findings

Theoretically proven optimality under Gaussian mixture models.

Quantifies the benefit of transfer learning in clustering.

Validated effectiveness through simulations and real data experiments.

Abstract

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.