Leverage triple relational structures via low-rank feature reduction for multi-output regression.
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| Title: | Leverage triple relational structures via low-rank feature reduction for multi-output regression. |
|---|---|
| Authors: | Zhang, Shichao1 zhangsc@mailbox.gxnu.edu.cn, Yang, Lifeng1, Deng, Zhenyun1, Cheng, Debo1, Li, Yonggang1 |
| Source: | Multimedia Tools & Applications. Aug2017, Vol. 76 Issue 16, p17461-17477. 17p. |
| Subjects: | Feature selection, Regression analysis data processing, Orthogonal functions, Data mining, Multimedia cartography |
| Abstract: | Multi-output regression aims at learning a mapping from feature variables to multiple output variables. It is significant to utilize variety of inherent relational structure information of observations to conduct multi-output regression task when learning a best mapping from high-dimensional data. In this paper, we propose a new multi-output regression method, which simultaneously takes advantage of the low-rank constraint, sample selection, and feature selection in a unified framework. We first take the effect of low-rank constraint to search the correlation of output variables and impose ℓ -norm regularization on the coefficient matrix to capture the correlation between features and outputs. And then, the ℓ -norm on the loss function is designed to discover the correlation between samples, so as to select those informative samples to learn the model for improving predictive capacity. Thirdly, orthogonal subspace learning is exploited to ensure multi-output variables share the same low-rank structure of data by rotating the results of feature selection. In addition, to get the optimal solution of the objective function, we propose an effective iterative optimization algorithm. Finally, we conduct sets of experimental results on real datasets, and show the proposed method outperforms the state-of-the-art methods in terms of aCC and aRMSE. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 124132909 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Leverage triple relational structures via low-rank feature reduction for multi-output regression. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Shichao%22">Zhang, Shichao</searchLink><relatesTo>1</relatesTo><i> zhangsc@mailbox.gxnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Lifeng%22">Yang, Lifeng</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Deng%2C+Zhenyun%22">Deng, Zhenyun</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Cheng%2C+Debo%22">Cheng, Debo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Li%2C+Yonggang%22">Li, Yonggang</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Aug2017, Vol. 76 Issue 16, p17461-17477. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis+data+processing%22">Regression analysis data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Orthogonal+functions%22">Orthogonal functions</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Multimedia+cartography%22">Multimedia cartography</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Multi-output regression aims at learning a mapping from feature variables to multiple output variables. It is significant to utilize variety of inherent relational structure information of observations to conduct multi-output regression task when learning a best mapping from high-dimensional data. In this paper, we propose a new multi-output regression method, which simultaneously takes advantage of the low-rank constraint, sample selection, and feature selection in a unified framework. We first take the effect of low-rank constraint to search the correlation of output variables and impose ℓ -norm regularization on the coefficient matrix to capture the correlation between features and outputs. And then, the ℓ -norm on the loss function is designed to discover the correlation between samples, so as to select those informative samples to learn the model for improving predictive capacity. Thirdly, orthogonal subspace learning is exploited to ensure multi-output variables share the same low-rank structure of data by rotating the results of feature selection. In addition, to get the optimal solution of the objective function, we propose an effective iterative optimization algorithm. Finally, we conduct sets of experimental results on real datasets, and show the proposed method outperforms the state-of-the-art methods in terms of aCC and aRMSE. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-016-3980-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 17461 Subjects: – SubjectFull: Feature selection Type: general – SubjectFull: Regression analysis data processing Type: general – SubjectFull: Orthogonal functions Type: general – SubjectFull: Data mining Type: general – SubjectFull: Multimedia cartography Type: general Titles: – TitleFull: Leverage triple relational structures via low-rank feature reduction for multi-output regression. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Shichao – PersonEntity: Name: NameFull: Yang, Lifeng – PersonEntity: Name: NameFull: Deng, Zhenyun – PersonEntity: Name: NameFull: Cheng, Debo – PersonEntity: Name: NameFull: Li, Yonggang IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 76 – Type: issue Value: 16 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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