Jointly low-rank and bisparse recovery: Questions and partial answers
(2019) Analysis and Applications — Vol. 18, n° 01, p. 25-48 (2019)
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- We investigate the problem of recovering jointly $r$-rank and $s$-bisparse matrices from as few linear measurements as possible, considering arbitrary measurements as well as rank-one measurements. In both cases, we show that $m \asymp r s \ln(en/s)$ measurements make the recovery possible in theory, meaning via a nonpractical algorithm. In case of arbitrary measurements, we investigate the possibility of achieving practical recovery via an iterative-hard-thresholding algorithm when $m \asymp r s^\gamma \ln(en/s)$ for some exponent $\gamma > 0$. We show that this is feasible for $\gamma = 2$, and that the proposed analysis cannot cover the case $\gamma \leq 1$. The precise value of the optimal exponent $\gamma \in [1,2]$ is the object of a question, raised but unresolved in this paper, about head projections for the jointly low-rank and bisparse structure. Some related questions are partially answered in passing. For rank-one measurements, we suggest on arcane grounds an iterative-hard-thresholding algorithm modified to exploit the nonstandard restricted isometry property obeyed by this type of measurements.
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Foucart, S., Gribonval, R., Jacques, L., & Rauhut, H. (2019). Jointly low-rank and bisparse recovery: Questions and partial answers. Analysis and Applications, 18(01), 25-48. https://doi.org/10.1142/s0219530519410094 (Original work published 2019)