Hierarchical Multiple Kernel Clustering

Authors

  • Jiyuan Liu National University of Defense Technology
  • Xinwang Liu National University of Defense Technology
  • Siwei Wang National University of Defense Technology
  • Sihang Zhou National University of Defense Technology
  • Yuexiang Yang National University of Defense Technology

Keywords:

Multi-instance/Multi-view Learning, Clustering, Kernel Methods, Multimodal Learning

Abstract

Current multiple kernel clustering algorithms compute a partition with the consensus kernel or graph learned from the pre-specified ones, while the emerging late fusion methods firstly construct multiple partitions from each kernel separately, and then obtain a consensus one with them. However, both of them directly distill the clustering information from kernels or graphs to partition matrices, where the sudden dimension drop would result in loss of advantageous details for clustering. In this paper, we provide a brief insight of the aforementioned issue and propose a hierarchical approach to perform clustering while preserving advantageous details maximumly. Specifically, we gradually group samples into fewer clusters, together with generating a sequence of intermediary matrices of descending sizes. The consensus partition with is simultaneously learned and conversely guides the construction of intermediary matrices. Nevertheless, this cyclic process is modeled into an unified objective and an alternative algorithm is designed to solve it. In addition, the proposed method is validated and compared with other representative multiple kernel clustering algorithms on benchmark datasets, demonstrating state-of-the-art performance by a large margin.

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Published

2021-05-18

How to Cite

Liu, J., Liu, X., Wang, S., Zhou, S., & Yang, Y. (2021). Hierarchical Multiple Kernel Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 35(10), 8671-8679. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17051

Issue

Section

AAAI Technical Track on Machine Learning III