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arXiv · 1804.09097

Sparse Power Factorization: Balancing peakiness and sample complexity

Jakob Geppert·Felix Krahmer·Dominik Stöger
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Abstract

In many applications, one is faced with an inverse problem, where the known signal depends in a bilinear way on two unknown input vectors. Often at least one of the input vectors is assumed to be sparse, i.e., to have only few non-zero entries. Sparse Power Factorization (SPF), proposed by Lee, Wu, and Bresler, aims to tackle this problem. They have established recovery guarantees for a somewhat restrictive class of signals under the assumption that the measurements are random. We generalize these recovery guarantees to a significantly enlarged and more realistic signal class at the expense of a moderately increased number of measurements.

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Jakob Geppert, Felix Krahmer, Dominik Stöger. 2018-04-24. Sparse Power Factorization: Balancing peakiness and sample complexity. https://arxiv.org/abs/1804.09097

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