Discriminative embedded multi-view fuzzy C-means clustering for feature-redundant and incomplete data

Multi-view clustering is a widely-used technique that seeks to categorize data obtained from various sources. As a representative method, multi-view fuzzy clustering has attracted growing attention. However, it becomes quite challenging when feature-redundant and incomplete data is presented. Despite the existing studies on dimension reduction and imputation methods, several issues remain unresolved. There is an excessive concern on the imputation, without considering that interpolation methods lead to accuracy degradation. Moreover, most of the methods usually process these two steps separately, resulting in inefficiency. To address these issues, we propose a discriminative embedded incomplete multi-view fuzzy c-means clustering method. We construct the indicator matrix to guide the learning of the common membership function, and design the projection matrix to construct embedding spaces. Subsequently, we develop an iterative optimization algorithm that solves the resultant problem. We demonstrate that the projection matrix can be achieved through the utilization of eigenvalue decomposition. Through extensive experimental studies on various benchmark datasets, the proposed method demonstrates the effectiveness and efficiency compared to the existing state-of-the-art clustering algorithms.