{ "id": "1002.4935", "version": "v3", "published": "2010-02-26T08:00:08.000Z", "updated": "2013-05-24T15:00:13.000Z", "title": "Multiarray Signal Processing: Tensor decomposition meets compressed sensing", "authors": [ "Lek-Heng Lim", "Pierre Comon" ], "comment": "10 pages, 1 figure", "categories": [ "math.NA", "cs.IT", "math.IT" ], "abstract": "We discuss how recently discovered techniques and tools from compressed sensing can be used in tensor decompositions, with a view towards modeling signals from multiple arrays of multiple sensors. We show that with appropriate bounds on a measure of separation between radiating sources called coherence, one could always guarantee the existence and uniqueness of a best rank-r approximation of the tensor representing the signal. We also deduce a computationally feasible variant of Kruskal's uniqueness condition, where the coherence appears as a proxy for k-rank. Problems of sparsest recovery with an infinite continuous dictionary, lowest-rank tensor representation, and blind source separation are treated in a uniform fashion. The decomposition of the measurement tensor leads to simultaneous localization and extraction of radiating sources, in an entirely deterministic manner.", "revisions": [ { "version": "v3", "updated": "2013-05-24T15:00:13.000Z" } ], "analyses": { "subjects": [ "94A12", "15A69", "41A29", "41A50", "41A52" ], "keywords": [ "tensor decomposition meets compressed sensing", "multiarray signal processing", "blind source separation", "lowest-rank tensor representation", "radiating sources" ], "tags": [ "journal article" ], "publication": { "doi": "10.1016/j.crme.2010.06.005", "journal": "Comptes Rendus Mecanique", "year": 2010, "month": "Jun", "volume": 338, "number": 6, "pages": 311 }, "note": { "typesetting": "TeX", "pages": 10, "language": "en", "license": "arXiv", "status": "editable", "adsabs": "2010CRMec.338..311L" } } }