EVA-MVC: Equitable View-weight Allocation for Generic Multi-View Clustering

yuan fang, Xiaofeng Feng, Geping Yang, Ruichu Cai, Yiyang Yang, Zhiguo Gong, Zhifeng Hao

Track: Graph algorithms and modeling for the Web

Keywords: Multi-view Subspace Clustering, Large-scale, Information Fusion, Equitable View-weight Allocation

TL;DR: This paper presents EVA-MVC, an algorithm that improves Multi-view Clustering (MVC) by allocating equitable view-weights through view segmentation based on view supplementarity.

Abstract:

Contemporary datasets sourced from the web often adopt a multi-view format, collecting data from diverse sources, domains, or modules.
Existing methodologies employed to analyze such datasets frequently overlook or inaccurately allocate the view-weights, pivotal metrics reflecting each view's significance.
This work introduces EVA-MVC, a simple yet effective algorithm designed for Equitable View-weight Allocation (EVA) seamlessly integrated with arbitrary Multi-view Clustering (MVC) methods.
Within the EVA phase, we establish theoretical connections between view supplementarity and Multi-view Subspace Learning (MSL), leading to the partiton of views into View Communities (VCs) based on these foundational principles.
These VCs exhibit internal supplementarity similarities, facilitating Equitable View-weights Allocation through VC-specific MSL.
The proposed EVA process precedes and operates independently of traditional or SOTA MVC approaches, requiring no additional processing or specialized design, making it an ideal preprocessing step for MVC applications.
Through comprehensive evaluations across diverse multi-view datasets, our findings reveal that our EVA significantly enhances the effectiveness of mainstream MVC frameworks, resulting in a notable performance improvement.

Submission Number: 1011