Synopsis of recent research by authors named "Guangcan Liu"
- Guangcan Liu's research primarily focuses on developing advanced machine learning techniques, particularly in image processing and human action recognition, as demonstrated by his works on multilevel spatial-temporal excited graph networks and robust subspace clustering for HAR.
- In his 2024 article, Liu introduces a novel method for large-scale canonical correlation analysis in the Fourier domain, emphasizing efficiency in both time and memory usage, which addresses challenges faced in traditional CCA implementations.
- Liu's work also includes contributions to understanding deep neural networks through optimization perspectives and the exploration of dictionary learning models, enhancing classification and representation learning in high-dimensional spaces.
