Online search Orthogonal Matching Pursuit
The recovery of a sparse signal x from y= Φx, where Φ is a matrix with more columns than rows, is a task central to many signal processing problems. In this paper we present a new greedy algorithm to solve this type of problem. Our approach leverages ideas from the field of online search on state sp...
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| creator | Weinstein, A. J. Wakin, M. B. |
| description | The recovery of a sparse signal x from y= Φx, where Φ is a matrix with more columns than rows, is a task central to many signal processing problems. In this paper we present a new greedy algorithm to solve this type of problem. Our approach leverages ideas from the field of online search on state spaces. We adopt the "agent perspective" and consider the set of possible supports of x as the state space. Under this setup, finding a solution is equivalent to finding a path from the empty support set to the state whose support has both the desired cardinality and the capacity to explain the observation vector y. An empirical investigation on Compressive Sensing problems shows that this new approach outperforms the classic greedy algorithm Orthogonal Matching Pursuit (OMP) while maintaining a reasonable computational complexity. |
| doi_str_mv | 10.1109/SSP.2012.6319766 |
| format | Conference Proceeding |
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An empirical investigation on Compressive Sensing problems shows that this new approach outperforms the classic greedy algorithm Orthogonal Matching Pursuit (OMP) while maintaining a reasonable computational complexity.</description><subject>Compressed sensing</subject><subject>Compressive Sensing</subject><subject>Greedy algorithms</subject><subject>Indexes</subject><subject>Matching pursuit algorithms</subject><subject>Orthogonal Matching Pursuit (OMP)</subject><subject>Planning</subject><subject>Search problems</subject><subject>sparse approximation</subject><subject>Vectors</subject><issn>2373-0803</issn><issn>2693-3551</issn><isbn>9781467301824</isbn><isbn>1467301825</isbn><isbn>1467301817</isbn><isbn>9781467301817</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j0tLw0AURscXWGv2gptsXUy8d25m5s5Sii-opFBdl0mYNCMxlUm68N9bsK4OfAc-OELcIBSI4O7X61WhAFVhCJ015kRcYWksATLaUzFTxpEkrfFMZM7yv1Pl-cGRJQkMdCmycfwEADSsiNVM3FVDH4eQj8GnpsurNHW77W7wff7mp6aLwzZf7dO4j9O1uGh9P4bsyLn4eHp8X7zIZfX8unhYyohWT5KIjeMWWQeAoAMjttqCc1wDKDAUjFN12RxWzwotlB4MW2xUbR2Cp7m4_fuNIYTNd4pfPv1sjtX0C3yKQ6I</recordid><startdate>201208</startdate><enddate>201208</enddate><creator>Weinstein, A. 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J.</creatorcontrib><creatorcontrib>Wakin, M. B.</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>Weinstein, A. J.</au><au>Wakin, M. B.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Online search Orthogonal Matching Pursuit</atitle><btitle>2012 IEEE Statistical Signal Processing Workshop (SSP)</btitle><stitle>SSP</stitle><date>2012-08</date><risdate>2012</risdate><spage>584</spage><epage>587</epage><pages>584-587</pages><issn>2373-0803</issn><eissn>2693-3551</eissn><isbn>9781467301824</isbn><isbn>1467301825</isbn><eisbn>1467301817</eisbn><eisbn>9781467301817</eisbn><abstract>The recovery of a sparse signal x from y= Φx, where Φ is a matrix with more columns than rows, is a task central to many signal processing problems. In this paper we present a new greedy algorithm to solve this type of problem. Our approach leverages ideas from the field of online search on state spaces. We adopt the "agent perspective" and consider the set of possible supports of x as the state space. Under this setup, finding a solution is equivalent to finding a path from the empty support set to the state whose support has both the desired cardinality and the capacity to explain the observation vector y. An empirical investigation on Compressive Sensing problems shows that this new approach outperforms the classic greedy algorithm Orthogonal Matching Pursuit (OMP) while maintaining a reasonable computational complexity.</abstract><pub>IEEE</pub><doi>10.1109/SSP.2012.6319766</doi><tpages>4</tpages></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Compressed sensing Compressive Sensing Greedy algorithms Indexes Matching pursuit algorithms Orthogonal Matching Pursuit (OMP) Planning Search problems sparse approximation Vectors |
| title | Online search Orthogonal Matching Pursuit |
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