  A novel tag completion algorithm is proposed in this paper, which is designed
with the following features: 1) Low-rank and error s-parsity: the incomplete
initial tagging matrix D is decomposed into the complete tagging matrix A and a
sparse error matrix E. However, instead of minimizing its nuclear norm, A is
further factor-ized into a basis matrix U and a sparse coefficient matrix V,
i.e. D=UV+E. This low-rank formulation encapsulating sparse coding enables our
algorithm to recover latent structures from noisy initial data and avoid
performing too much denoising; 2) Local reconstruction structure consistency:
to steer the completion of D, the local linear reconstruction structures in
feature space and tag space are obtained and preserved by U and V respectively.
Such a scheme could alleviate the negative effect of distances measured by
low-level features and incomplete tags. Thus, we can seek a balance between
exploiting as much information and not being mislead to suboptimal performance.
Experiments conducted on Corel5k dataset and the newly issued Flickr30Concepts
dataset demonstrate the effectiveness and efficiency of the proposed method.
