University of Oulu

S. Wang, X. Liu, L. Liu, S. Zhou and E. Zhu, "Late Fusion Multiple Kernel Clustering With Proxy Graph Refinement," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2021.3117403

Late fusion multiple kernel clustering with proxy graph refinement

Saved in:
Author: Wang, Siwei1; Liu, Xinwang1; Liu, Li2,3;
Organizations: 1School of Computer, National University of Defense Technology, Changsha 410073, China
2College of System Engineering, National University of Defense Technology, Changsha 410073, China
3Center for Machine Vision and Signal Analysis, University of Oulu, 90014 Oulu, Finland
4College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 3.4 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2022-10-05


Multiple kernel clustering (MKC) optimally utilizes a group of pre-specified base kernels to improve clustering performance. Among existing MKC algorithms, the recently proposed late fusion MKC methods demonstrate promising clustering performance in various applications and enjoy considerable computational acceleration. However, we observe that the kernel partition learning and late fusion processes are separated from each other in the existing mechanism, which may lead to suboptimal solutions and adversely affect the clustering performance. In this article, we propose a novel late fusion multiple kernel clustering with proxy graph refinement (LFMKC-PGR) framework to address these issues. First, we theoretically revisit the connection between late fusion kernel base partition and traditional spectral embedding. Based on this observation, we construct a proxy self-expressive graph from kernel base partitions. The proxy graph in return refines the individual kernel partitions and also captures partition relations in graph structure rather than simple linear transformation. We also provide theoretical connections and considerations between the proposed framework and the multiple kernel subspace clustering. An alternate algorithm with proved convergence is then developed to solve the resultant optimization problem. After that, extensive experiments are conducted on 12 multi-kernel benchmark datasets, and the results demonstrate the effectiveness of our proposed algorithm. The code of the proposed algorithm is publicly available at

see all

Series: IEEE transactions on neural networks and learning systems
ISSN: 2162-237X
ISSN-E: 2162-2388
ISSN-L: 2162-237X
Issue: Online first
DOI: 10.1109/tnnls.2021.3117403
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported in part by the National Key Research and Development Program of China under Project 2020AAA0107100; and in part by the National Natural Science Foundation of China under Project 61922088, Project 61906020, Project 61825305, Project 62006237, and Project 61773392.
Copyright information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.