Elastic deep multi-view autoencoder with diversity embedding

Abstract

Current research on multi-view clustering (MVC) is pushing the boundaries of knowledge, allowing the extraction of valuable insights from various points of view. Recently, many researchers in this field have turned to deep learning techniques, particularly autoencoders (AE). These methods are highly effective at recognizing nonlinear and complex structures, making them a popular choice in this area. However, while they effectively handle Gaussian noise through the use of the Frobenius norm, they exhibit sensitivity to outlier data and Laplacian noise. Additionally, existing multi-view methodologies tend to usually rely on shared information across multi-view data, this means they often miss out on the diverse and complementary information each view can provide, leading in an inadequate utilization of the information available. In this paper, we present an innovative solution to address these challenges. Our novel Elastic Deep Multi-view Autoencoder with Diversity Embedding (EDMVAE-DE) method incorporates an Elastic Deep AE to enhance its robustness. Furthermore, we integrate an exclusivity constraint term to enhance the diversity of specific representations across distinct views, increasing both modeling consistency and diversity within a unified framework. To address problems arising from incorrectly classified neighbors, we also incorporate a graph constraint term. Our experiments, performed on various multi-view datasets, clearly show that EDMVAE-DE achieves the best performance in its class1.