Learning Bregman Distance Functions for Structural Learning to Rank.
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| Title: | Learning Bregman Distance Functions for Structural Learning to Rank. |
|---|---|
| Authors: | Li, Xi1, Pi, Te2, Zhang, Zhongfei2, Zhao, Xueyi2, Wang, Meng3, Li, Xuelong4, Yu, Philip S.5 |
| Source: | IEEE Transactions on Knowledge & Data Engineering. Sep2017, Vol. 29 Issue 9, p1916-1927. 12p. |
| Subjects: | Structural learning theory, Robust control, Support vector machines, Semantics, Metric spaces |
| Abstract: | We study content-based learning to rank from the perspective of learning distance functions. Standardly, the two key issues of learning to rank, feature mappings and score functions, are usually modeled separately, and the learning is usually restricted to modeling a linear distance function such as the Mahalanobis distance. However, the modeling of feature mappings and score functions are mutually interacted, and the patterns underlying the data are probably complicated and nonlinear. Thus, as a general nonlinear distance family, the Bregman distance is a suitable distance function for learning to rank, due to its strong generalization ability for distance functions, and its nonlinearity for exploring the general patterns of data distributions. In this paper, we study learning to rank as a structural learning problem, and devise a Bregman distance function to build the ranking model based on structural SVM. To improve the model robustness to outliers, we develop a robust structural learning framework for the ranking model. The proposed model Robust Structural Bregman distance functions Learning to Rank (RSBLR) is a general and unified framework for learning distance functions to rank. The experiments of data ranking on real-world datasets show the superiority of this method to the state-of-the-art literature, as well as its robustness to the noisily labeled outliers. [ABSTRACT FROM PUBLISHER] |
| Copyright of IEEE Transactions on Knowledge & Data Engineering is the property of IEEE 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 124539493 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Learning Bregman Distance Functions for Structural Learning to Rank. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Xi%22">Li, Xi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Pi%2C+Te%22">Pi, Te</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Zhongfei%22">Zhang, Zhongfei</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Xueyi%22">Zhao, Xueyi</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Meng%22">Wang, Meng</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Li%2C+Xuelong%22">Li, Xuelong</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Yu%2C+Philip+S%2E%22">Yu, Philip S.</searchLink><relatesTo>5</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Knowledge+%26+Data+Engineering%22">IEEE Transactions on Knowledge & Data Engineering</searchLink>. Sep2017, Vol. 29 Issue 9, p1916-1927. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Structural+learning+theory%22">Structural learning theory</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Metric+spaces%22">Metric spaces</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We study content-based learning to rank from the perspective of learning distance functions. Standardly, the two key issues of learning to rank, feature mappings and score functions, are usually modeled separately, and the learning is usually restricted to modeling a linear distance function such as the Mahalanobis distance. However, the modeling of feature mappings and score functions are mutually interacted, and the patterns underlying the data are probably complicated and nonlinear. Thus, as a general nonlinear distance family, the Bregman distance is a suitable distance function for learning to rank, due to its strong generalization ability for distance functions, and its nonlinearity for exploring the general patterns of data distributions. In this paper, we study learning to rank as a structural learning problem, and devise a Bregman distance function to build the ranking model based on structural SVM. To improve the model robustness to outliers, we develop a robust structural learning framework for the ranking model. The proposed model Robust Structural Bregman distance functions Learning to Rank (RSBLR) is a general and unified framework for learning distance functions to rank. The experiments of data ranking on real-world datasets show the superiority of this method to the state-of-the-art literature, as well as its robustness to the noisily labeled outliers. [ABSTRACT FROM PUBLISHER] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Knowledge & Data Engineering is the property of IEEE 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: 10.1109/TKDE.2017.2654250 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1916 Subjects: – SubjectFull: Structural learning theory Type: general – SubjectFull: Robust control Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Semantics Type: general – SubjectFull: Metric spaces Type: general Titles: – TitleFull: Learning Bregman Distance Functions for Structural Learning to Rank. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Xi – PersonEntity: Name: NameFull: Pi, Te – PersonEntity: Name: NameFull: Zhang, Zhongfei – PersonEntity: Name: NameFull: Zhao, Xueyi – PersonEntity: Name: NameFull: Wang, Meng – PersonEntity: Name: NameFull: Li, Xuelong – PersonEntity: Name: NameFull: Yu, Philip S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 10414347 Numbering: – Type: volume Value: 29 – Type: issue Value: 9 Titles: – TitleFull: IEEE Transactions on Knowledge & Data Engineering Type: main |
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