Solving Soft Clustering Ensemble via $k$-Sparse Discrete Wasserstein Barycenter

Part of Advances in Neural Information Processing Systems 34 (NeurIPS 2021)

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Ruizhe Qin, Mengying Li, Hu Ding


Clustering ensemble is one of the most important problems in ensemble learning. Though it has been extensively studied in the past decades, the existing methods often suffer from the issues like high computational complexity and the difficulty on understanding the consensus. In this paper, we study the more general soft clustering ensemble problem where each individual solution is a soft clustering. We connect it to the well-known discrete Wasserstein barycenter problem in geometry. Based on some novel geometric insights in high dimensions, we propose the sampling-based algorithms with provable quality guarantees. We also provide the systematical analysis on the consensus of our model. Finally, we conduct the experiments to evaluate our proposed algorithms.