Journal of Algorithms
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Image Completion Using Global Optimization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Convex multi-task feature learning
Machine Learning
Tensor Decompositions and Applications
SIAM Review
Optimum subspace learning and error correction for tensors
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Interactive Video Indexing With Statistical Active Learning
IEEE Transactions on Multimedia
Tensor Completion for Estimating Missing Values in Visual Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The tensor completion problem is to recover a low-n-rank tensor from a subset of its entries. The main solution strategy has been based on the extensions of trace norm for the minimization of tensor rank via convex optimization. This strategy bears the computational cost required by the singular value decomposition (SVD) which becomes increasingly expensive as the size of the underlying tensor increase. In order to reduce the computational cost, we propose a multi-linear low-n-rank factorization model and apply the nonlinear Gauss-Seidal method that only requires solving a linear least squares problem per iteration to solve this model. Numerical results show that the proposed algorithm can reliably solve a wide range of problems at least several times faster than the trace norm minimization algorithm.