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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Cross Low-Dimension Pursuit for Sparse Signal Recovery from Incomplete Measurements Based on Permuted Block Diagonal Matrix
| Title: | Cross Low-Dimension Pursuit for Sparse Signal Recovery from Incomplete Measurements Based on Permuted Block Diagonal Matrix |
| Authors: | He, Zaixing Browse this author | | Ogawa, Takahiro Browse this author | | Haseyama, Miki Browse this author →KAKEN DB |
| Keywords: | sparse recovery | | sparsest solution | | compressed sensing | | permuted block diagonal matrix | | greedy algorithms | | orthogonal matching pursuit | | ℓ1-norm minimization | | basis pursuit |
| Issue Date: | 1-Sep-2011 |
| Publisher: | Institute of Electronics, Information and Communication Engineers |
| Journal Title: | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |
| Volume: | E94-A |
| Issue: | 9 |
| Start Page: | 1793 |
| End Page: | 1803 |
| Publisher DOI: | 10.1587/transfun.E94.A.1793 |
| Abstract: | In this paper, a novel algorithm, Cross Low-dimension Pursuit, based on a new structured sparse matrix, Permuted Block Diagonal (PBD) matrix, is proposed in order to recover sparse signals from incomplete linear measurements. The main idea of the proposed method is using the PBD matrix to convert a high-dimension sparse recovery problem into two (or more) groups of highly low-dimension problems and crossly recover the entries of the original signal from them in an iterative way. By sampling a sufficiently sparse signal with a PBD matrix, the proposed algorithm can recover it efficiently. It has the following advantages over conventional algorithms: (1) low complexity, i.e., the algorithm has linear complexity, which is much lower than that of existing algorithms including greedy algorithms such as Orthogonal Matching Pursuit and (2) high recovery ability, i.e., the proposed algorithm can recover much less sparse signals than even ℓ1-norm minimization algorithms. Moreover, we demonstrate both theoretically and empirically that the proposed algorithm can reliably recover a sparse signal from highly incomplete measurements. |
| Rights: | copyright©2011 IEICE |
| Relation: | http://search.ieice.org/ |
| Type: | article |
| URI: | http://hdl.handle.net/2115/48487 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 長谷山 美紀
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