Consistent and specific multi-view multi-label learning with correlation information

Yiting Li, Jia Zhang, Hanrui Wu, Guodong Du, Jinyi Long

Abstract: Highlights•A consistent and specific learning framework is proposed to handle MVML data.•We exploit the multi-view consistency by leveraging low-rank constraint.•Our proposal can recover specific subspace from each view.•We provide a convex relaxed alternating optimization algorithm to seek the optimal solution.•Extensive experiments are conducted to validate the effectiveness of our method.