dc.contributor.author |
Lu J. |
|
dc.contributor.author |
Lin J. |
|
dc.contributor.author |
Lai Z. |
|
dc.contributor.author |
Wang H. |
|
dc.contributor.author |
Zhou J. |
|
dc.date.accessioned |
2022-02-09T20:31:06Z |
|
dc.date.available |
2022-02-09T20:31:06Z |
|
dc.date.issued |
2021 |
|
dc.identifier.issn |
0020-0255 |
|
dc.identifier.uri |
https://dspace.kpfu.ru/xmlui/handle/net/168698 |
|
dc.description.abstract |
Least squares regression (LSR) has attracted widespread attention in the fields of statistics, machine learning, and pattern recognition. However, it utilizes strict zero-one regression targets, which leads to inferior performance on classification tasks. Furthermore, LSR ignores the local manifold structures of data and lacks robustness. To address these issues, this paper proposes a general regression framework called RLRR, where a low-rank constraint is imposed on regression matrices to explore the underlying correlation structures of classes. Strict zero-one regression targets are redirected to more feasible variable matrices for the purpose of margin amplification of different classes. Additionally, rather than using a pre-constructed weighted graph, the proposed framework dynamically updates the neighborhood structures of data to preserve original manifold structures. By utilizing this framework as a general platform, we developed two dynamic neighborhood-structure-based regression models called RLRRM and RLRRP. RLRRM integrates a reconstruction error minimization term into the proposed RLRR framework, whereas RLRRP aims to preserve the local geometric structures of data in a low-dimensional subspace. Both RLRRM and RLRRP use theℓ2,1-norm penalty to replace the traditional F-norm penalty for the projection matrix for the sake of self-adaptive feature selection. Instead of directly solving the resultant optimization problems with non-convex constraints, we adopt the variable-splitting and penalty techniques to derive an equivalent solution. Analysis of the corresponding convergence and computational complexity characteristics is also presented. Extensive experiments on several well-known datasets demonstrate the promising performance of the proposed models. |
|
dc.relation.ispartofseries |
Information Sciences |
|
dc.subject |
Dynamic neighbors |
|
dc.subject |
Joint sparsity |
|
dc.subject |
Least squares regression |
|
dc.subject |
Local and global structure preservation |
|
dc.subject |
Target redirected regression |
|
dc.title |
Target redirected regression with dynamic neighborhood structure |
|
dc.type |
Article |
|
dc.relation.ispartofseries-volume |
544 |
|
dc.collection |
Публикации сотрудников КФУ |
|
dc.relation.startpage |
564 |
|
dc.source.id |
SCOPUS00200255-2021-544-SID85092003879 |
|