KanDMVC: KAN be used for deep multi-view clustering?

Abstract

Deep multi-view clustering has garnered increasing attention for its ability to explore common semantics from multiple views for clustering tasks using deep learning techniques. However, existing research faces two challenges. (1) Coding dependency challenge: Current coding frameworks are composed of linear layers and the same activation function stacked together, resulting in an extreme dependence on the choice of activation function for its ability to capture features. (2) Data discrepancy challenge: Different view data exhibit certain variability due to originating from different data sources. These variabilities can affect the results of feature fusion, thereby reducing clustering performance. To address these challenges, this paper proposes a view comparison fusion framework based on the Kolmogorov–Arnold Network for deep multi-view clustering (KanDMVC). First, we design a new adaptive activation function coding framework based on KAN (KANencoder), which adaptively learns the matching activation function during training on different datasets to reducing the challenge of coding dependency. Additionally, to fully consider the differences and similarities between view features, we design a contrast fusion module (Feature fusion) that takes into account feature variability and similarity, addressing the data discrepancy challenge to learn a more comprehensive feature representation. Finally, extensive experiments on multiple datasets demonstrate that our proposed method outperforms state-of-the-art deep multi-view clustering algorithms. The code for this article is published on Github at https://github.com/snothingtosay/KanDMVC.git.