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In this paper we consider sparsity on a tensor level, as given by the n-rank of a tensor. In the important sparse-vector approximation problem (compressed sensing) and the low-rank matrix recovery problem, using a convex relaxation technique proved to be a valuable solution strategy. Here, we will adapt these techniques to the tensor setting. We use the(More)
We address the problem of recovering a low-rank matrix that has a small fraction of its entries arbitrarily corrupted. This problem is recently attracting attention as nontrivial extension of the classical PCA (principal component analysis) problem with applications in image processing and model/system identification. It was shown that the problem can be(More)
In this paper, we propose a novel online scheme for the sparse adap-tive filtering problem. It is based on a formulation of the adaptive filtering problem as a minimization of the sum of (possibly nons-mooth) convex functions. Our proposed scheme is a time-varying extension of the so-called Douglas-Rachford splitting method. It covers many existing adaptive(More)
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