446 research outputs found
Iterative Singular Tube Hard Thresholding Algorithms for Tensor Completion
Due to the explosive growth of large-scale data sets, tensors have been a
vital tool to analyze and process high-dimensional data. Different from the
matrix case, tensor decomposition has been defined in various formats, which
can be further used to define the best low-rank approximation of a tensor to
significantly reduce the dimensionality for signal compression and recovery. In
this paper, we consider the low-rank tensor completion problem. We propose a
novel class of iterative singular tube hard thresholding algorithms for tensor
completion based on the low-tubal-rank tensor approximation, including basic,
accelerated deterministic and stochastic versions. Convergence guarantees are
provided along with the special case when the measurements are linear.
Numerical experiments on tensor compressive sensing and color image inpainting
are conducted to demonstrate convergence and computational efficiency in
practice
A Splitting Augmented Lagrangian Method for Low Multilinear-Rank Tensor Recovery
This paper studies a recovery task of finding a low multilinear-rank tensor
that fulfills some linear constraints in the general settings, which has many
applications in computer vision and graphics. This problem is named as the low
multilinear-rank tensor recovery problem. The variable splitting technique and
convex relaxation technique are used to transform this problem into a tractable
constrained optimization problem. Considering the favorable structure of the
problem, we develop a splitting augmented Lagrangian method to solve the
resulting problem. The proposed algorithm is easily implemented and its
convergence can be proved under some conditions. Some preliminary numerical
results on randomly generated and real completion problems show that the
proposed algorithm is very effective and robust for tackling the low
multilinear-rank tensor completion problem
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