### Abstract

In sparse approximation theory, the fundamental problem is to reconstruct a signal A ∈ ℝ^{n} from linear measurements 〈A,ψ_{i}〉 with respect to a dictionary of ψ_{i}'s. Recently, there is focus on the novel direction of Compressed Sensing [9] where the reconstruction can be done with very few - O(k log n) linear measurements over a modified dictionary if the signal is compressible, that is, its information is concentrated in k coefficients with the original dictionary. In particular, these results [9,4,23] prove that there exists a single O(k log n) × n measurement matrix such that any such signal can be reconstructed from these measurements, with error at most O(1) times the worst case error for the class of such signals. Compressed sensing has generated tremendous excitement both because of the sophisticated underlying Mathematics and because of its potential applications. In this paper, we address outstanding open problems in Compressed Sensing. Our main result is an explicit construction of a non-adaptive measurement matrix and the corresponding reconstruction algorithm so that with a number of measurements polynomial in k, log n, 1/ε, we can reconstruct compressible signals. This is the first known polynomial time explicit construction of any such measurement matrix. In addition, our result improves the error guarantee from O(1) to 1 + ε and improves the reconstruction time from poly(n) to poly(k log n). Our second result is a randomized construction of O(k polylog(n)) measurements that work for each signal with high probability and gives per-instance approximation guarantees rather than over the class of all signals. Previous work on Compressed Sensing does not provide such per-instance approximation guarantees; our result improves the best known number of measurements known from prior work in other areas including Learning Theory [20, 21], Streaming algorithms [11, 12, 6] and Complexity Theory [1] for this case. Our approach is combinatorial. In particular, we use two parallel sets of group tests, one to filter and the other to certify and estimate; the resulting algorithms are quite simple to implement.

Original language | English (US) |
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Title of host publication | Structural Information and Communication Complexity - 13th International Colloquium, SIROCCO 2006, Proceedings |

Publisher | Springer-Verlag |

Pages | 280-294 |

Number of pages | 15 |

ISBN (Print) | 3540354743, 9783540354741 |

DOIs | |

State | Published - Jan 1 2006 |

Event | 13th International Colloquium on Structural Information and Communication Complexity, SIROCCO 2006 - Chester, United Kingdom Duration: Jul 2 2006 → Jul 5 2006 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 4056 LNCS |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Conference

Conference | 13th International Colloquium on Structural Information and Communication Complexity, SIROCCO 2006 |
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Country | United Kingdom |

City | Chester |

Period | 7/2/06 → 7/5/06 |

### Fingerprint

### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

### Cite this

*Structural Information and Communication Complexity - 13th International Colloquium, SIROCCO 2006, Proceedings*(pp. 280-294). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4056 LNCS). Springer-Verlag. https://doi.org/10.1007/11780823_22

**Combinatorial algorithms for compressed sensing.** / Cormode, Graham; Muthukrishnan, Shanmugavelayutham.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Structural Information and Communication Complexity - 13th International Colloquium, SIROCCO 2006, Proceedings.*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4056 LNCS, Springer-Verlag, pp. 280-294, 13th International Colloquium on Structural Information and Communication Complexity, SIROCCO 2006, Chester, United Kingdom, 7/2/06. https://doi.org/10.1007/11780823_22

}

TY - GEN

T1 - Combinatorial algorithms for compressed sensing

AU - Cormode, Graham

AU - Muthukrishnan, Shanmugavelayutham

PY - 2006/1/1

Y1 - 2006/1/1

N2 - In sparse approximation theory, the fundamental problem is to reconstruct a signal A ∈ ℝn from linear measurements 〈A,ψi〉 with respect to a dictionary of ψi's. Recently, there is focus on the novel direction of Compressed Sensing [9] where the reconstruction can be done with very few - O(k log n) linear measurements over a modified dictionary if the signal is compressible, that is, its information is concentrated in k coefficients with the original dictionary. In particular, these results [9,4,23] prove that there exists a single O(k log n) × n measurement matrix such that any such signal can be reconstructed from these measurements, with error at most O(1) times the worst case error for the class of such signals. Compressed sensing has generated tremendous excitement both because of the sophisticated underlying Mathematics and because of its potential applications. In this paper, we address outstanding open problems in Compressed Sensing. Our main result is an explicit construction of a non-adaptive measurement matrix and the corresponding reconstruction algorithm so that with a number of measurements polynomial in k, log n, 1/ε, we can reconstruct compressible signals. This is the first known polynomial time explicit construction of any such measurement matrix. In addition, our result improves the error guarantee from O(1) to 1 + ε and improves the reconstruction time from poly(n) to poly(k log n). Our second result is a randomized construction of O(k polylog(n)) measurements that work for each signal with high probability and gives per-instance approximation guarantees rather than over the class of all signals. Previous work on Compressed Sensing does not provide such per-instance approximation guarantees; our result improves the best known number of measurements known from prior work in other areas including Learning Theory [20, 21], Streaming algorithms [11, 12, 6] and Complexity Theory [1] for this case. Our approach is combinatorial. In particular, we use two parallel sets of group tests, one to filter and the other to certify and estimate; the resulting algorithms are quite simple to implement.

AB - In sparse approximation theory, the fundamental problem is to reconstruct a signal A ∈ ℝn from linear measurements 〈A,ψi〉 with respect to a dictionary of ψi's. Recently, there is focus on the novel direction of Compressed Sensing [9] where the reconstruction can be done with very few - O(k log n) linear measurements over a modified dictionary if the signal is compressible, that is, its information is concentrated in k coefficients with the original dictionary. In particular, these results [9,4,23] prove that there exists a single O(k log n) × n measurement matrix such that any such signal can be reconstructed from these measurements, with error at most O(1) times the worst case error for the class of such signals. Compressed sensing has generated tremendous excitement both because of the sophisticated underlying Mathematics and because of its potential applications. In this paper, we address outstanding open problems in Compressed Sensing. Our main result is an explicit construction of a non-adaptive measurement matrix and the corresponding reconstruction algorithm so that with a number of measurements polynomial in k, log n, 1/ε, we can reconstruct compressible signals. This is the first known polynomial time explicit construction of any such measurement matrix. In addition, our result improves the error guarantee from O(1) to 1 + ε and improves the reconstruction time from poly(n) to poly(k log n). Our second result is a randomized construction of O(k polylog(n)) measurements that work for each signal with high probability and gives per-instance approximation guarantees rather than over the class of all signals. Previous work on Compressed Sensing does not provide such per-instance approximation guarantees; our result improves the best known number of measurements known from prior work in other areas including Learning Theory [20, 21], Streaming algorithms [11, 12, 6] and Complexity Theory [1] for this case. Our approach is combinatorial. In particular, we use two parallel sets of group tests, one to filter and the other to certify and estimate; the resulting algorithms are quite simple to implement.

UR - http://www.scopus.com/inward/record.url?scp=33746345633&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33746345633&partnerID=8YFLogxK

U2 - 10.1007/11780823_22

DO - 10.1007/11780823_22

M3 - Conference contribution

SN - 3540354743

SN - 9783540354741

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 280

EP - 294

BT - Structural Information and Communication Complexity - 13th International Colloquium, SIROCCO 2006, Proceedings

PB - Springer-Verlag

ER -