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A novel tag completion algorithm is proposed in this paper, which is designedwith the following features: 1) Low-rank and error s-parsity: the incompleteinitial tagging matrix D is decomposed into the complete tagging matrix A and asparse error matrix E. However, instead of minimizing its nuclear norm, A isfurther 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 ouralgorithm to recover latent structures from noisy initial data and avoidperforming too much denoising; 2) Local reconstruction structure consistency:to steer the completion of D, the local linear reconstruction structures infeature space and tag space are obtained and preserved by U and V respectively.Such a scheme could alleviate the negative effect of distances measured bylow-level features and incomplete tags. Thus, we can seek a balance betweenexploiting as much information and not being mislead to suboptimal performance.Experiments conducted on Corel5k dataset and the newly issued Flickr30Conceptsdataset demonstrate the effectiveness and efficiency of the proposed method.

Image Tag Completion by Low-rank Factorization with Dual Reconstruction Structure Preserved