Reduced complexity bounded error subset selection
A reduced complexity version of the bounded error subset selection (BESS) algorithm is proposed. By relaxing the integer constraint in the original BESS algorithm, we show that the BESS problem can be reformulated as an ordinary linear program instead of an integer program with exponential worst-cas...
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| creator | Alghoniemy, M. Tewfik, A.H. |
| description | A reduced complexity version of the bounded error subset selection (BESS) algorithm is proposed. By relaxing the integer constraint in the original BESS algorithm, we show that the BESS problem can be reformulated as an ordinary linear program instead of an integer program with exponential worst-case complexity. We retain the sparseness of the representation in the modified BESS by weighting the dictionary with the minimum 2-norm solution of the subset selection problem corresponding to the BESS problem at hand. The proposed algorithm is compared to the basis pursuit, orthogonal matching pursuit, and the best orthogonal basis algorithms. It is shown that the proposed algorithm has a better packing property and an improved rate-distortion behavior. |
| doi_str_mv | 10.1109/ICASSP.2005.1416406 |
| format | Conference Proceeding |
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It is shown that the proposed algorithm has a better packing property and an improved rate-distortion behavior.</description><subject>Approximation algorithms</subject><subject>Computer errors</subject><subject>Dictionaries</subject><subject>Greedy algorithms</subject><subject>Iterative algorithms</subject><subject>Matching pursuit algorithms</subject><subject>Pursuit algorithms</subject><subject>Signal processing algorithms</subject><subject>Signal representations</subject><subject>Vectors</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9780780388741</isbn><isbn>0780388747</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj8tqwzAUREUf0JD6C7LxD9i9el8tS-gjEGhpsuguWNIVuDhxsGxo_r6GZhgYOIthhrEVh5pzcE-b9fNu91kLAF1zxY0Cc8MWQlpXcQfft6xwFmG2RLSK37EF1wIqw5V7YEXOPzDLCGuNWjD-RXEKFMvQH88d_bbjpfT9dIozomHohzJPPtNYZuoojG1_emT3qekyFddcsv3ry379Xm0_3uZp26p1MFZemuBdgCQbg-hiVGSk1wEMBos2IjU6peQRAbVGJ6TUwltrKWoin-SSrf5rWyI6nIf22AyXw_Wv_AN140f9</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Alghoniemy, M.</creator><creator>Tewfik, A.H.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2005</creationdate><title>Reduced complexity bounded error subset selection</title><author>Alghoniemy, M. ; Tewfik, A.H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-b36cb9c0f3a6889dd4e63b5c068c787d8ea5fffb8808558923352b777ed5eebf3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Approximation algorithms</topic><topic>Computer errors</topic><topic>Dictionaries</topic><topic>Greedy algorithms</topic><topic>Iterative algorithms</topic><topic>Matching pursuit algorithms</topic><topic>Pursuit algorithms</topic><topic>Signal processing algorithms</topic><topic>Signal representations</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Alghoniemy, M.</creatorcontrib><creatorcontrib>Tewfik, A.H.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Alghoniemy, M.</au><au>Tewfik, A.H.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Reduced complexity bounded error subset selection</atitle><btitle>Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005</btitle><stitle>ICASSP</stitle><date>2005</date><risdate>2005</risdate><volume>5</volume><spage>v/725</spage><epage>v/728 Vol. 5</epage><pages>v/725-v/728 Vol. 5</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9780780388741</isbn><isbn>0780388747</isbn><abstract>A reduced complexity version of the bounded error subset selection (BESS) algorithm is proposed. By relaxing the integer constraint in the original BESS algorithm, we show that the BESS problem can be reformulated as an ordinary linear program instead of an integer program with exponential worst-case complexity. We retain the sparseness of the representation in the modified BESS by weighting the dictionary with the minimum 2-norm solution of the subset selection problem corresponding to the BESS problem at hand. The proposed algorithm is compared to the basis pursuit, orthogonal matching pursuit, and the best orthogonal basis algorithms. It is shown that the proposed algorithm has a better packing property and an improved rate-distortion behavior.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2005.1416406</doi></addata></record> |
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| ispartof | Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, 2005, Vol.5, p.v/725-v/728 Vol. 5 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Approximation algorithms Computer errors Dictionaries Greedy algorithms Iterative algorithms Matching pursuit algorithms Pursuit algorithms Signal processing algorithms Signal representations Vectors |
| title | Reduced complexity bounded error subset selection |
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