Incomplete multi-view clustering (IMVC) aims to leverage complementary information from multi-view data with missing instances to enhance clustering performance. Many existing IMVC methods exhibit limitations in effectively exploiting hidden information and addressing distribution differences between views and modules. To address these challenges, we present a novel IMVC framework that leverages the proposed stack feature-based matrix completion to impute the missing instances, enhancing the exploitation of underlying information. We also incorporate graph consensus to integrate graph structures learned from both completed and observed data. Additionally, we introduce correntropy-induced metric as a flexible measurement to adaptively assign different constraints to various views and modules. Furthermore, we derive an efficient iterative algorithm based on Fenchel conjugate and accelerated block coordinate update (BCU) to solve the joint learning problem. Experimental results on eight benchmark datasets demonstrate the superior performance of our method compared to state-of-the-art IMVC methods across various metrics.

Lei Xing

Yawen Song

Badong Chen

Changyuan Yu

Jing Qin

IEEE Transactions on Multimedia

期刊的范围是多媒体技术和多媒体应用研究的各个方面，包括但不限于电路、网络、信号处理、系统、软件和系统集成，以“感兴趣的领域”为代表。赞助商。

The IEEE Transactions on Multimedia journal covers various aspects of multimedia technology and application research, including but not limited to circuits, networks, signal processing, systems, software, and system integration. The journal is sponsored by stakeholders in the field.

《IEEE Transactions on Multimedia》期刊涵盖多媒体技术及其应用研究的各个方面，包括但不限于电路、网络、信号处理、系统、软件和系统集成等领域。该期刊由相关领域的赞助商支持。

Category	Quartile
计算机科学	1
计算机科学, 计算机信息系统	1
计算机科学, 计算机软件工程	1
计算机科学, 电信学	2

Incomplete Multi-View Clustering Via Correntropy and Complement Consensus Learning

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