Dual Graph Laplacian RPCA Method for Face Recognition Based on Anchor Points

High-dimensional data often contain noise and redundancy, which can significantly undermine the performance of machine learning. To address this challenge, we propose an advanced robust principal component analysis (RPCA) model that integrates bidirectional graph Laplacian constraints alongwith the anchor point technique. This approach constructs two graphs from both the sample and feature perspectives for a more comprehensive capture of the underlying data structure. Moreover, the anchor point technique serves to substantially reduce computational complexity, making the model more efficient and scalable. Experiments conducted on the GTdatabase demonstrate that our model maintains high accuracy and improves efficiency, particularly under challenging conditions like varying illumination and pose. The method enhances dimensionality reduction and robustness in face recognition, making it suitable for large-scale applications.