Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements
Finding sparse solutions of under-determined systems of linear equations is a problem of significance importance in signal processing and statistics. In this paper we study an iterative reweighted least squares (IRLS) approach to find sparse solutions of underdetermined system of equations based on...
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| creator | Carrillo, R.E. Barner, K.E. |
| description | Finding sparse solutions of under-determined systems of linear equations is a problem of significance importance in signal processing and statistics. In this paper we study an iterative reweighted least squares (IRLS) approach to find sparse solutions of underdetermined system of equations based on smooth approximation of the L 0 norm and the method is extended to find sparse solutions from noisy measurements. Analysis of the proposed methods show that weaker conditions on the sensing matrices are required. Simulation results demonstrate that the proposed method requires fewer samples than existing methods, while maintaining a reconstruction error of the same order and demanding less computational complexity. |
| doi_str_mv | 10.1109/CISS.2009.5054762 |
| format | Conference Proceeding |
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Simulation results demonstrate that the proposed method requires fewer samples than existing methods, while maintaining a reconstruction error of the same order and demanding less computational complexity.</description><subject>Compressed sensing</subject><subject>Computational complexity</subject><subject>Computational modeling</subject><subject>Equations</subject><subject>Iterative methods</subject><subject>Least squares approximation</subject><subject>Least squares methods</subject><subject>re-weighted least squares</subject><subject>sampling methods</subject><subject>Signal processing</subject><subject>Signal reconstruction</subject><subject>Sparse matrices</subject><subject>Statistics</subject><subject>underdetermined systems of linear equations</subject><isbn>9781424427338</isbn><isbn>1424427339</isbn><isbn>1424427347</isbn><isbn>9781424427345</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UEtLw0AYXJGCtvYHiJf9A4n7fhwlqA0UPFSvlk3ypa7kUXe3Sv69Aetchhlm5jAI3VKSU0rsfVHudjkjxOaSSKEVu0BLKpgQTHOhL9HaavOvuVmg5Zw1lihi2RVax_hJZgjJLOfX6L1MEFzy39BNOED2A_7wkaDBHbiYcPw6uQARt2PA8ehCBBz9YXDdnK3HIaZwqpMfB9yGscfD6OOE-7l5CtDDkOINWrSui7A-8wq9PT2-Fpts-_JcFg_bzFMtU8YV50xqQWtogTlNq9mQTFWiMhXoxhDrdG1IqyxvGiVNbQxzStJKEicN4St097frAWB_DL53Ydqf_-G_IAZYXQ</recordid><startdate>200903</startdate><enddate>200903</enddate><creator>Carrillo, R.E.</creator><creator>Barner, K.E.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200903</creationdate><title>Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements</title><author>Carrillo, R.E. ; Barner, K.E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-363325741cefe2a71b633526b4b8be7d809a7c80f693dd658c882a651b50a5803</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Compressed sensing</topic><topic>Computational complexity</topic><topic>Computational modeling</topic><topic>Equations</topic><topic>Iterative methods</topic><topic>Least squares approximation</topic><topic>Least squares methods</topic><topic>re-weighted least squares</topic><topic>sampling methods</topic><topic>Signal processing</topic><topic>Signal reconstruction</topic><topic>Sparse matrices</topic><topic>Statistics</topic><topic>underdetermined systems of linear equations</topic><toplevel>online_resources</toplevel><creatorcontrib>Carrillo, R.E.</creatorcontrib><creatorcontrib>Barner, K.E.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Carrillo, R.E.</au><au>Barner, K.E.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements</atitle><btitle>2009 43rd Annual Conference on Information Sciences and Systems</btitle><stitle>CISS</stitle><date>2009-03</date><risdate>2009</risdate><spage>448</spage><epage>453</epage><pages>448-453</pages><isbn>9781424427338</isbn><isbn>1424427339</isbn><eisbn>1424427347</eisbn><eisbn>9781424427345</eisbn><abstract>Finding sparse solutions of under-determined systems of linear equations is a problem of significance importance in signal processing and statistics. 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| identifier | ISBN: 9781424427338 |
| ispartof | 2009 43rd Annual Conference on Information Sciences and Systems, 2009, p.448-453 |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Compressed sensing Computational complexity Computational modeling Equations Iterative methods Least squares approximation Least squares methods re-weighted least squares sampling methods Signal processing Signal reconstruction Sparse matrices Statistics underdetermined systems of linear equations |
| title | Iteratively re-weighted least squares for sparse signal reconstruction from noisy measurements |
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