Multi-view Stable Feature Selection with Adaptive Optimization of View Weights

The feature selection problem in multi -view data has garnered widespread attention and research in recent years, leading to the development of numerous feature selection algorithms tailored for multi -view data. However, existing methods often focus solely on known data, overlooking the potential distribution information of unknown data. Additionally, these methods inevitably introduce a large number of parameters to fully utilize the information from different views in multi -view data, thereby reducing the efficiency of model training. To address these issues comprehensively, we propose a novel framework called Multi -view Stable Feature Selection with Adaptive Optimization of View Weights (MvSFS-AOW). Specifically, the framework first employs the Multi -view Stable Feature Selection (MvSFS) algorithm to evaluate and select features from different views. Subsequently, it dynamically adjusts view weights using the Adaptive Optimization of View Weights (AOW) algorithm to achieve optimal generalization performance. By incorporating unknown data into the training process, we enhance the reliability of the framework in practical applications. Furthermore, our framework achieves competitive performance without requiring extensive parameter tuning. Experimental results demonstrate that the proposed framework achieves promising classification and clustering performance on multiple datasets, surpassing other state-of-the-art algorithms. Code for this paper available on: https: //github.com/boredcui/MvSFS-AOW.