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Target redirected regression with dynamic neighborhood structure

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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


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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