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arXiv 2011.10254cs.LGcs.AIstat.ML

通过视图演化方案的不平衡不完整多视图聚类：弱视图为食，强视图为食

Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat

School of Computer Science and Technology, Key Laboratory of Information Storage System Ministry of Education of China, Huazhong University of Science and Technology（计算机科学与技术学院，信息存储系统教育部重点实验室，华中科技大学）；Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology（大数据安全工程研究中心，网络安全学院，华中科技大学）；Department of Electrical and Computer Engineering, University of Florida（电气与计算机工程系，佛罗里达大学）

Xiang Fang, Yuchong Hu, Pan Zhou, Dapeng Oliver Wu

备注：
Accepted by IEEE Transactions on Emerging Topics in Computational Intelligence
发表信息：
IEEE Transactions on Emerging Topics in Computational Intelligence 2021
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AI总结：

针对不同视图不完整程度不平衡的问题，受生物进化理论启发，提出基于视图演化的不平衡不完整多视图聚类方法UIMC，通过加权多视图子空间聚类和低秩鲁棒表示恢复数据，显著提升聚类性能。

摘要：

不完整多视图聚类是处理现实世界中不完整多视图数据的重要技术。以往的工作假设所有视图具有相同的不完整性，即平衡不完整性。然而，不同的视图往往具有不同的不完整性，即不平衡不完整性，这导致了强视图（低不完整性视图）和弱视图（高不完整性视图）。不平衡不完整性阻止我们直接使用先前的方法进行聚类。在本文中，受有效生物进化理论的启发，我们设计了新颖的视图演化方案来聚类强视图和弱视图。此外，我们提出了一种不平衡不完整多视图聚类方法（UIMC），这是第一个基于视图演化的有效方法，用于不平衡不完整多视图聚类。与先前的方法相比，UIMC有两个独特的优势：1）它提出了加权多视图子空间聚类来整合这些不平衡不完整的视图，有效解决了不平衡不完整多视图问题；2）它设计了低秩和鲁棒表示来恢复数据，减少了不完整性和噪声的影响。大量的实验结果表明，UIMC在三个评估指标上相比其他最先进的方法将聚类性能提高了高达40%。

英文摘要：

Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results in strong views (low-incompleteness views) and weak views (high-incompleteness views). The unbalanced incompleteness prevents us from directly using the previous methods for clustering. In this paper, inspired by the effective biological evolution theory, we design the novel scheme of view evolution to cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is the first effective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate these unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view problem; 2) it designs the low-rank and robust representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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