Graph embedding orthogonal decomposition: A synchronous feature selection technique based on collaborative particle swarm optimization

Jingyu Zhong, Ronghua Shang, Songhua Xu, Yangyang Li

Abstract: Highlights•This paper proposes a synchronous feature selection technique based on graph-embedded cluster label orthogonal decomposition and collaborative particle swarm optimization (GOD-cPSO).•GOD-cPSO extends the feature selection framework of clustering label orthogonal decomposition by graph embedding.•The l2,1-2-norm with strong global convergence is extended to the graph embedding clustering label orthogonal decomposition framework.•The local structure preserving of low-dimensional manifolds is integrated into the graph-embedded clustering label orthogonal decomposition framework.•GOD-cPSO synchronously guides the graph-embedding clustering labeling orthogonal decomposition framework for feature selection through collaborative particle swarm optimization.