Unsupervised Feature Selection Algorithm Based on Graph Filtering and Self-representation
This work addresses feature selection in unsupervised learning, which is incremental as it builds on existing methods by incorporating higher-order graph information.
The authors tackled the problem of unsupervised feature selection by proposing an algorithm that combines graph filtering and self-representation to capture higher-order neighborhood information in data, achieving improved performance in simulation experiments.
Aiming at the problem that existing methods could not fully capture the intrinsic structure of data without considering the higher-order neighborhood information of the data, we proposed an unsupervised feature selection algorithm based on graph filtering and self-representation. Firstly,a higher-order graph filter was applied to the data to obtain its smooth representation,and a regularizer was designed to combine the higher-order graph information for the self-representation matrix learning to capture the intrinsic structure of the data. Secondly,l2,1 norm was used to reconstruct the error term and feature selection matrix to enhance the robustness and row sparsity of the model to select the discriminant features. Finally, an iterative algorithm was applied to effectively solve the proposed objective function and simulation experiments were carried out to verify the effectiveness of the proposed algorithm.