X. Liu et al., "Efficient and Effective Regularized Incomplete Multi-View Clustering," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 8, pp. 2634-2646, 1 Aug. 2021, doi: 10.1109/TPAMI.2020.2974828
Efficient and effective regularized incomplete multi-view clustering
|Author:||Liu, Xinwang1; Li, Miaomiao2; Tang, Chang3;|
1College of Computer, National University of Defense Technology, Changsha, 410073, China
2Department of Computer, Changsha College, Changsha, China, 410073
3School of Computer Science, China University of Geosciences, 430074
4epartment of Electric and Electronic Engineering, Imperial College London, London, SW72AZ, UK
5School of Business Administration, Southwestern University of Finance and Economics, Chengdu, Sichuan, 611130, China
6College of System Engineering, National University of Defense Technology, Changsha, China
7Center for Machine Vision and Signal Analysis, University of Oulu, 90014 Oulu, Finland
8Department of Computer Science, Technische Universitat Kaiserslautern, Kaiserslautern, Germany, 67653
|Online Access:||PDF Full Text (PDF, 6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021102752478
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-10-27
Incomplete multi-view clustering (IMVC) optimally combines multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel k -means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines IMVC as a joint optimization problem where the clustering and kernel matrix imputation tasks are alternately performed until convergence. Though demonstrating promising performance in various applications, we observe that the manner of kernel matrix imputation in MKKM-IK would incur intensive computational and storage complexities, over-complicated optimization and limitedly improved clustering performance. In this paper, we first propose an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm to address these issues. Instead of completing the incomplete kernel matrices, EE-IMVC proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix. Moreover, we further improve this algorithm by incorporating prior knowledge to regularize the learned consensus clustering matrix. Two three-step iterative algorithms are carefully developed to solve the resultant optimization problems with linear computational complexity, and their convergence is theoretically proven. After that, we theoretically study the generalization bound of the proposed algorithms. Furthermore, we conduct comprehensive experiments to study the proposed algorithms in terms of clustering accuracy, evolution of the learned consensus clustering matrix and the convergence. As indicated, our algorithms deliver their effectiveness by significantly and consistently outperforming some state-of-the-art ones.
IEEE transactions on pattern analysis and machine intelligence
|Pages:||2634 - 2646|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This work was supported by the Natural Science Foundation of China (project no. 61773392, 61922088 and 61701451).
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