Late Fusion Incomplete Multi-View Clustering

Incomplete multi-view clustering optimally integrates a group of pre-specified incomplete views to improve clustering performance. Among various excellent solutions, multiple kernel kk-means with incomplete kernels forms a benchmark, which redefines the incomplete multi-view clustering as a joint op...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 41; no. 10; pp. 2410 - 2423
Main Authors: Liu, Xinwang, Zhu, Xinzhong, Li, Miaomiao, Wang, Lei, Tang, Chang, Yin, Jianping, Shen, Dinggang, Wang, Huaimin, Gao, Wen
Format: Journal Article
Language:English
Published: United States IEEE 01.10.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Clustering
Clustering algorithms
Convergence
incomplete kernel learning
Iterative algorithms
Iterative methods
Kernel
Kernels
Multiple kernel clustering
multiple view learning
Optimization
Parameter sensitivity
Partitioning algorithms
Pattern analysis
Permutations
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary:Incomplete multi-view clustering optimally integrates a group of pre-specified incomplete views to improve clustering performance. Among various excellent solutions, multiple kernel kk-means with incomplete kernels forms a benchmark, which redefines the incomplete multi-view clustering as a joint optimization problem where the imputation and clustering are alternatively performed until convergence. However, the comparatively intensive computational and storage complexities preclude it from practical applications. To address these issues, we propose Late Fusion Incomplete Multi-view Clustering (LF-IMVC) which effectively and efficiently integrates the incomplete clustering matrices generated by incomplete views. Specifically, our algorithm jointly learns a consensus clustering matrix, imputes each incomplete base matrix, and optimizes the corresponding permutation matrices. We develop a three-step iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence. Further, we conduct comprehensive experiments to study the proposed LF-IMVC in terms of clustering accuracy, running time, advantages of late fusion multi-view clustering, evolution of the learned consensus clustering matrix, parameter sensitivity and convergence. As indicated, our algorithm significantly and consistently outperforms some state-of-the-art algorithms with much less running time and memory.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2018.2879108