## Abstract

The sparse representation problem of recovering an N dimensional sparse vector x from M < N linear observations y = Dx given dictionary D is considered. The standard approach is to let the elements of the dictionary be independent and identically distributed (IID) zero-mean Gaussian and minimize the l_{1}-norm of x under the constraint y = Dx. In this paper, the performance of l_{1}-reconstruction is analyzed, when the dictionary is bi-orthogonal D = [O_{1} O_{2}], where O_{1}, O _{2} are independent and drawn uniformly according to the Haar measure on the group of orthogonal M × M matrices. By an application of the replica method, we obtain the critical conditions under which perfect l _{1}-recovery is possible with bi-orthogonal dictionaries.

Original language | English |
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Title of host publication | 2012 IEEE Information Theory Workshop, ITW 2012 |

Pages | 647-651 |

ISBN (Electronic) | 978-1-4673-0223-4 |

DOIs | |

Publication status | Published - 1 Dec 2012 |

MoE publication type | A4 Article in a conference publication |

Event | 2012 IEEE Information Theory Workshop, ITW 2012 - Lausanne, Switzerland Duration: 3 Sep 2012 → 7 Sep 2012 |

### Conference

Conference | 2012 IEEE Information Theory Workshop, ITW 2012 |
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Country | Switzerland |

City | Lausanne |

Period | 3/09/12 → 7/09/12 |