Reliable Attribute-missing Multi-view Clustering with Instance-level and feature-level Cooperative Imputation

Dayu Hu, Suyuan Liu, Jun Wang, Junpu Zhang, Siwei Wang, Xingchen Hu, Xinzhong Zhu, Chang Tang, Xinwang Liu

Abstract: Multi-view clustering (MVC) constitutes a distinct approach to data mining within the field of machine learning. Due to limitations in the data collection process, missing attributes are frequently encountered. However, existing MVC methods primarily focus on missing instances, showing limited attention to missing attributes. A small number of studies employ the reconstruction of missing instances to address missing attributes, potentially overlooking the synergistic effects between the instance and feature spaces, which could lead to distorted imputation outcomes. Furthermore, current methods uniformly treat all missing attributes as zero values, thus failing to differentiate between real and technical zeroes, potentially resulting in data over-imputation. To mitigate these challenges, we introduce a novel Reliable Attribute-Missing Multi-View Clustering method (RAM-MVC). Specifically, feature reconstruction is utilized to address missing attributes, while similarity graphs are simultaneously constructed within the instance and feature spaces. By leveraging structural information from both spaces, RAM-MVC learns a high-quality feature reconstruction matrix during the joint optimization process. Additionally, we introduce a reliable imputation guidance module that distinguishes between real and technical attribute-missing events, enabling discriminative imputation. The proposed RAM-MVC method outperforms nine baseline methods, as evidenced by real-world experiments using single-cell multi-view data.

Primary Subject Area: [Content] Multimodal Fusion

Secondary Subject Area: [Experience] Multimedia Applications

Relevance To Conference: This work introduces RAM-MVC, a pioneering unified attribute-missing multi-view clustering framework that seamlessly integrates bi-level imputation with reliable guidance, ensuring both components work together effectively to achieve accurate imputation and enhanced clustering performance. It is crucial in multimedia and multimodal processing, significantly boosting attribute-missing imputation and improving multi-view clustering outcomes.

Submission Number: 2139