A semi-supervised label-driven auto-weighted strategy for multi-view data classification

at a lower computational cost compared to other related methods, and the weight change experiments show that our proposed strategy can distinguish view importance more accurately than other related strategies especially when low-quality views are involved in multi-view learning.