  A variety of modern applications exhibit multi-view multi-label learning,
where each sample has multi-view features, and multiple labels are correlated
via common views. Current methods usually fail to directly deal with the
setting where only a subset of features and labels are observed for each
sample, and ignore the presence of noisy views and imbalanced labels in
real-world problems. In this paper, we propose a novel method to overcome the
limitations. It jointly embeds incomplete views and weak labels into a
low-dimensional subspace with adaptive weights, and facilitates the difference
between embedding weight matrices via auto-weighted Hilbert-Schmidt
Independence Criterion (HSIC) to reduce the redundancy. Moreover, it adaptively
learns view-wise importance for embedding to detect noisy views, and mitigates
the label imbalance problem by focal loss. Experimental results on four
real-world multi-view multi-label datasets demonstrate the effectiveness of the
proposed method.
