Weighted Concept Factorization Based Incomplete Multi-view Clustering

Abstract

The primary objective of classical multiview clustering (MVC) is to categorize data into separate clusters under the assumption that all perspectives are completely available. However, in practical situations, it is common to encounter cases where not all viewpoints of the data are accessible. This limitation can impede the effectiveness of traditional MVC methods. The incompleteness of the clustering of multiview data has witnessed substantial progress in recent years due to its promising applications. In response to the aforementioned issue, we have tackled it by introducing an inventive MVC algorithm that is tailored to handle incomplete data from various views. Additionally, we have proposed a distinct objective function that leverages a weighted concept factorization technique to address the absence of data instances within each incomplete perspective. To address inconsistencies between different views, we introduced a coregularization factor, which operates in conjunction with a shared consensus matrix. It is important to highlight that the proposed objective function is intrinsically nonconvex, presenting challenges in terms of optimization. To secure the optimal solution for this objective function, we have implemented an iterative optimization approach to reach the local minima for our method. To underscore the efficacy and validation of our approach, we experimented with real-world datasets and used state-of-the-art methods to perform comparative assessments.