Late Fusion Incomplete Multi-view Clustering

By Xinwang Liu, Xinzhong Zhu, Miaomiao Li, Lei Wang, Chang-Fu Tang, Jianping Yin, Dinggang Shen, Huaimin Wang, Wen Gao. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018

346 citations recorded by the source corpus.

Incomplete multi-view clustering optimally integrates a group of pre-specified incomplete views to improve clustering performance. Among various excellent solutions, multiple kernel $k$k-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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Urban Studies

View Paper: https://www.semanticscholar.org/paper/44bbcf83bd8d3c44eafaa5d9831c51e2ab692ea7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. En Zhu
2. Yue Liu
3. Joey Tianyi Zhou
4. Wenxuan Tu
5. Zongbo Han
6. Qinghua Hu
7. Changqing Zhang
8. Huan Fu

In Computer Vision and Pattern Recognition

1. Qinghua Hu
2. Changqing Zhang
3. Huan Fu
4. En Zhu
5. Yue Liu
6. Joey Tianyi Zhou
7. Wenxuan Tu
8. Zongbo Han

Citations in the corpus

• Machine Learning and Integrative Analysis of Biomedical Big Data (2019, background): “LF-IMVC employs a kernel matrix for each view, and performs imputation and clustering simultaneously.”
• Trusted Multi-View Classification With Dynamic Evidential Fusion (2022, methodology): “The naive fusion strategy is the most widely used method, which fuses decision results from different sources through decision averaging [12], [51], [52], decision voting [53], [54], or weighting multiple decisions [55], [56], [57], [58].”
• Deep Graph Clustering via Dual Correlation Reduction (2021, methodology): “The clustering performance is evaluated by four public metrics: Accuracy (ACC), Normalized Mutual Information (NMI), Average Rand Index (ARI) and macro F1-score (F1) (Liu et al. 2019a, 2018, 2019b; Zhou et al. 2019, 2020).”
• Deep Partial Multi-View Learning (2020, background): “However, in practical applications, there are usually incomplete cases for multi-view data [4], [5], [6], [7], [8].”