Hierarchical Affinity Propagation

TL;DR

This paper introduces a hierarchical extension of affinity propagation, enabling efficient clustering across multiple levels, with demonstrated improvements on biological and spectral data over existing methods.

Contribution

The authors develop a novel hierarchical affinity propagation algorithm that efficiently handles high-order potentials and outperforms greedy and related methods in various applications.

Findings

Outperforms greedy clustering techniques in hierarchical settings

Achieves better objective scores on HIV sequence data

Performs favorably on mass spectra analysis

Abstract

Affinity propagation is an exemplar-based clustering algorithm that finds a set of data-points that best exemplify the data, and associates each datapoint with one exemplar. We extend affinity propagation in a principled way to solve the hierarchical clustering problem, which arises in a variety of domains including biology, sensor networks and decision making in operational research. We derive an inference algorithm that operates by propagating information up and down the hierarchy, and is efficient despite the high-order potentials required for the graphical model formulation. We demonstrate that our method outperforms greedy techniques that cluster one layer at a time. We show that on an artificial dataset designed to mimic the HIV-strain mutation dynamics, our method outperforms related methods. For real HIV sequences, where the ground truth is not available, we show our method achieves better results, in terms of the underlying objective function, and show the results correspond meaningfully to geographical location and strain subtypes. Finally we report results on using the method for the analysis of mass spectra, showing it performs favorably compared to state-of-the-art methods.