2019
DOI: 10.1142/s0219530519410094
|View full text |Cite
|
Sign up to set email alerts
|

Jointly low-rank and bisparse recovery: Questions and partial answers

Abstract: 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 ≍ rs 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 ≍ rs γ ln(en/s) for some exponent γ > 0… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

2
32
0

Year Published

2019
2019
2023
2023

Publication Types

Select...
2
2
1

Relationship

1
4

Authors

Journals

citations
Cited by 13 publications
(34 citation statements)
references
References 25 publications
2
32
0
Order By: Relevance
“…Let us now establish the upper bound in (2). We first show that, with high probability on a Gaussian random map A, all pairs of matrices in a set K ⊆ B n×n F that are consistent under sgn A must be at most ε apart provided m is large compared to ε and to the intrinsic set complexity.…”
Section: B Upper Estimatementioning
confidence: 98%
See 4 more Smart Citations
“…Let us now establish the upper bound in (2). We first show that, with high probability on a Gaussian random map A, all pairs of matrices in a set K ⊆ B n×n F that are consistent under sgn A must be at most ε apart provided m is large compared to ε and to the intrinsic set complexity.…”
Section: B Upper Estimatementioning
confidence: 98%
“…With Corollary 1 at hand, we can justify the upper bound in (2). For this purpose, consider ε > 0 making (9) an equality.…”
Section: B Upper Estimatementioning
confidence: 99%
See 3 more Smart Citations