Robust Semi-Supervised Deep Autoencoder-like Nonnegative Matrix Factorization for Multi-View Clustering

Abstract

Deep autoencoder-like nonnegative matrix factorization (DANMF) has emerged as a powerful dimensionality reduction paradigm, gaining significant traction in multi-view clustering (MVC) applications. Although existing DANMF-based MVC frameworks demonstrate competitive performance, they remain inherently susceptible to noise contamination and often fail to substantially improve clustering outcomes by effectively exploiting sparse supervisory information. To address these limitations, this paper introduces the correntropy-based semi-supervised multi-view deep autoencoder-like NMF (CSDANMF) framework for advanced multi-view clustering tasks. Compared to conventional DANMF-based MVC approaches, the proposed CSDANMF method introduces two distinct innovations: (1) CSDANMF substitutes the traditional linear Frobenius norm with a non-linear, localized similarity metric—specifically, maximum correntropy—as the loss function, thereby significantly enhancing the model’s robustness against outliers and noise. (2) CSDANMF leverages sparse label information to construct initial pairwise constraints and subsequently deploys a constraint propagation algorithm (CPA) to diffuse these supervisory signals across the data manifold, thereby maximizing the utility of limited prior knowledge to guide the clustering process. Furthermore, we provide comprehensive algorithmic evaluations, including a formal robustness analysis on corrupted datasets and a computational complexity analysis. Extensive experimental results across six benchmark nonnegative multi-view datasets demonstrate that CSDANMF consistently outperforms six state-of-the-art MVC methods, validating its efficacy and superior clustering performance.