Optimal Sparse Sliced Inverse Regression via Random Projection
<p>Given continuously emerging features, sufficient dimension reduction has been widely used as a supervised dimension reduction approach. Most existing high-dimensional sufficient dimension reduction methods involve penalized schemes, resulting in cumbersome tuning. To settle this problem, we...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1852015874671640576 |
|---|---|
| author | Jia Zhang (187802) |
| author2 | Runxiong Wu (22405116) Xin Chen (14149) |
| author2_role | author author |
| author_facet | Jia Zhang (187802) Runxiong Wu (22405116) Xin Chen (14149) |
| author_role | author |
| dc.creator.none.fl_str_mv | Jia Zhang (187802) Runxiong Wu (22405116) Xin Chen (14149) |
| dc.date.none.fl_str_mv | 2025-10-09T22:00:33Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.30324843.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Optimal_Sparse_Sliced_Inverse_Regression_via_Random_Projection/30324843 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Cancer Space Science Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified Minimax optimality Random projection Sparse sliced inverse regression Sufficient dimension reduction |
| dc.title.none.fl_str_mv | Optimal Sparse Sliced Inverse Regression via Random Projection |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Given continuously emerging features, sufficient dimension reduction has been widely used as a supervised dimension reduction approach. Most existing high-dimensional sufficient dimension reduction methods involve penalized schemes, resulting in cumbersome tuning. To settle this problem, we propose a novel sparse sliced inverse regression method for sufficient dimension reduction based on random projections in a large <i>p</i> small <i>n</i> setting. Embedded in a generalized eigenvalue framework, the proposed approach finally reduces to parallel execution of low-dimensional (generalized) eigenvalue decompositions, which facilitates high computational efficiency. Theoretically, we prove that this method achieves the minimax optimal rate of convergence under suitable assumptions. Furthermore, our algorithm involves a delicate reweighting scheme, which can significantly enhance the identifiability of the active set of covariates. Extensive numerical experiments demonstrate high superiority of the proposed algorithm in comparison to competing methods. Supplementary materials for this article are available online.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_5a03fd14e26343103c183f102e00a8a2 |
| identifier_str_mv | 10.6084/m9.figshare.30324843.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30324843 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Optimal Sparse Sliced Inverse Regression via Random ProjectionJia Zhang (187802)Runxiong Wu (22405116)Xin Chen (14149)MedicineCancerSpace ScienceMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedMinimax optimalityRandom projectionSparse sliced inverse regressionSufficient dimension reduction<p>Given continuously emerging features, sufficient dimension reduction has been widely used as a supervised dimension reduction approach. Most existing high-dimensional sufficient dimension reduction methods involve penalized schemes, resulting in cumbersome tuning. To settle this problem, we propose a novel sparse sliced inverse regression method for sufficient dimension reduction based on random projections in a large <i>p</i> small <i>n</i> setting. Embedded in a generalized eigenvalue framework, the proposed approach finally reduces to parallel execution of low-dimensional (generalized) eigenvalue decompositions, which facilitates high computational efficiency. Theoretically, we prove that this method achieves the minimax optimal rate of convergence under suitable assumptions. Furthermore, our algorithm involves a delicate reweighting scheme, which can significantly enhance the identifiability of the active set of covariates. Extensive numerical experiments demonstrate high superiority of the proposed algorithm in comparison to competing methods. Supplementary materials for this article are available online.</p>2025-10-09T22:00:33ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.30324843.v1https://figshare.com/articles/dataset/Optimal_Sparse_Sliced_Inverse_Regression_via_Random_Projection/30324843CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303248432025-10-09T22:00:33Z |
| spellingShingle | Optimal Sparse Sliced Inverse Regression via Random Projection Jia Zhang (187802) Medicine Cancer Space Science Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified Minimax optimality Random projection Sparse sliced inverse regression Sufficient dimension reduction |
| status_str | publishedVersion |
| title | Optimal Sparse Sliced Inverse Regression via Random Projection |
| title_full | Optimal Sparse Sliced Inverse Regression via Random Projection |
| title_fullStr | Optimal Sparse Sliced Inverse Regression via Random Projection |
| title_full_unstemmed | Optimal Sparse Sliced Inverse Regression via Random Projection |
| title_short | Optimal Sparse Sliced Inverse Regression via Random Projection |
| title_sort | Optimal Sparse Sliced Inverse Regression via Random Projection |
| topic | Medicine Cancer Space Science Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified Minimax optimality Random projection Sparse sliced inverse regression Sufficient dimension reduction |