Group Forward–Backward Orthogonal Matching Pursuit for General Convex Smooth Functions
This paper introduces the Group Forward–Backward Orthogonal Matching Pursuit (Group-FoBa-OMP) algorithm, a novel approach for sparse feature selection. The core innovations of this algorithm include (1) an integrated backward elimination process to correct earlier misidentified groups; (2) a versati...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2075-1680/13/11/774 |
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| _version_ | 1846154335334957056 |
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| author | Zhongxing Peng Gengzhong Zheng Wei Huang |
| author_facet | Zhongxing Peng Gengzhong Zheng Wei Huang |
| author_sort | Zhongxing Peng |
| collection | DOAJ |
| description | This paper introduces the Group Forward–Backward Orthogonal Matching Pursuit (Group-FoBa-OMP) algorithm, a novel approach for sparse feature selection. The core innovations of this algorithm include (1) an integrated backward elimination process to correct earlier misidentified groups; (2) a versatile convex smooth model that generalizes previous research; (3) the strategic use of gradient information to expedite the group selection phase; and (4) a theoretical validation of its performance in terms of support set recovery, variable estimation accuracy, and objective function optimization. These advancements are supported by experimental evidence from both synthetic and real-world data, demonstrating the algorithm’s effectiveness. |
| format | Article |
| id | doaj-art-8829313e99c244e5a881f1e6793ff184 |
| institution | Kabale University |
| issn | 2075-1680 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj-art-8829313e99c244e5a881f1e6793ff1842024-11-26T17:50:52ZengMDPI AGAxioms2075-16802024-11-01131177410.3390/axioms13110774Group Forward–Backward Orthogonal Matching Pursuit for General Convex Smooth FunctionsZhongxing Peng0Gengzhong Zheng1Wei Huang2School of Computer Information Engineering, Hanshan Normal University, Chaozhou 521041, ChinaSchool of Computer Information Engineering, Hanshan Normal University, Chaozhou 521041, ChinaSchool of Computer Information Engineering, Hanshan Normal University, Chaozhou 521041, ChinaThis paper introduces the Group Forward–Backward Orthogonal Matching Pursuit (Group-FoBa-OMP) algorithm, a novel approach for sparse feature selection. The core innovations of this algorithm include (1) an integrated backward elimination process to correct earlier misidentified groups; (2) a versatile convex smooth model that generalizes previous research; (3) the strategic use of gradient information to expedite the group selection phase; and (4) a theoretical validation of its performance in terms of support set recovery, variable estimation accuracy, and objective function optimization. These advancements are supported by experimental evidence from both synthetic and real-world data, demonstrating the algorithm’s effectiveness.https://www.mdpi.com/2075-1680/13/11/774compressed sensinggroup orthogonal matching pursuitgroup sparsegroup restricted isometry propertyinstance optimality |
| spellingShingle | Zhongxing Peng Gengzhong Zheng Wei Huang Group Forward–Backward Orthogonal Matching Pursuit for General Convex Smooth Functions Axioms compressed sensing group orthogonal matching pursuit group sparse group restricted isometry property instance optimality |
| title | Group Forward–Backward Orthogonal Matching Pursuit for General Convex Smooth Functions |
| title_full | Group Forward–Backward Orthogonal Matching Pursuit for General Convex Smooth Functions |
| title_fullStr | Group Forward–Backward Orthogonal Matching Pursuit for General Convex Smooth Functions |
| title_full_unstemmed | Group Forward–Backward Orthogonal Matching Pursuit for General Convex Smooth Functions |
| title_short | Group Forward–Backward Orthogonal Matching Pursuit for General Convex Smooth Functions |
| title_sort | group forward backward orthogonal matching pursuit for general convex smooth functions |
| topic | compressed sensing group orthogonal matching pursuit group sparse group restricted isometry property instance optimality |
| url | https://www.mdpi.com/2075-1680/13/11/774 |
| work_keys_str_mv | AT zhongxingpeng groupforwardbackwardorthogonalmatchingpursuitforgeneralconvexsmoothfunctions AT gengzhongzheng groupforwardbackwardorthogonalmatchingpursuitforgeneralconvexsmoothfunctions AT weihuang groupforwardbackwardorthogonalmatchingpursuitforgeneralconvexsmoothfunctions |