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Generalized sparse metric learning with relative comparisons

Publication ,  Journal Article
Huang, K; Ying, Y; Campbell, C
Published in: Knowledge and Information Systems
January 1, 2011

The objective of sparse metric learning is to learn a distance measure from a set of data in addition to finding a low-dimensional representation. Despite demonstrated success, the performance of existing sparse metric learning approaches is usually limited because the methods assumes certain problem relaxations or they target the SML objective indirectly. In this paper, we propose a Generalized Sparse Metric Learning method. This novel framework offers a unified view for understanding many existing sparse metric learning algorithms including the Sparse Metric Learning framework proposed in (Rosales and Fung ACM International conference on knowledge discovery and data mining (KDD), pp 367-373, 2006), the Large Margin Nearest Neighbor (Weinberger et al. in Advances in neural information processing systems (NIPS), 2006; Weinberger and Saul in Proceedings of the twenty-fifth international conference on machine learning (ICML-2008), 2008), and the D-ranking Vector Machine (D-ranking VM) (Ouyang and Gray in Proceedings of the twenty-fifth international conference on machine learning (ICML-2008), 2008). Moreover, GSML also establishes a close relationship with the Pairwise Support Vector Machine (Vert et al. in BMC Bioinform, 8, 2007). Furthermore, the proposed framework is capable of extending many current non-sparse metric learning models to their sparse versions including Relevant Component Analysis (Bar-Hillel et al. in J Mach Learn Res, 6:937-965, 2005) and a state-of-the-art method proposed in (Xing et al. Advances in neural information processing systems (NIPS), 2002). We present the detailed framework, provide theoretical justifications, build various connections with other models, and propose an iterative optimization method, making the framework both theoretically important and practically scalable for medium or large datasets. Experimental results show that this generalized framework outperforms six state-of-the-art methods with higher accuracy and significantly smaller dimensionality for seven publicly available datasets. © 2010 Springer-Verlag London Limited.

Duke Scholars

Published In

Knowledge and Information Systems

DOI

EISSN

0219-3116

ISSN

0219-1377

Publication Date

January 1, 2011

Volume

28

Issue

1

Start / End Page

25 / 45

Related Subject Headings

  • Information Systems
  • 46 Information and computing sciences
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Huang, K., Ying, Y., & Campbell, C. (2011). Generalized sparse metric learning with relative comparisons. Knowledge and Information Systems, 28(1), 25–45. https://doi.org/10.1007/s10115-010-0313-0
Huang, K., Y. Ying, and C. Campbell. “Generalized sparse metric learning with relative comparisons.” Knowledge and Information Systems 28, no. 1 (January 1, 2011): 25–45. https://doi.org/10.1007/s10115-010-0313-0.
Huang K, Ying Y, Campbell C. Generalized sparse metric learning with relative comparisons. Knowledge and Information Systems. 2011 Jan 1;28(1):25–45.
Huang, K., et al. “Generalized sparse metric learning with relative comparisons.” Knowledge and Information Systems, vol. 28, no. 1, Jan. 2011, pp. 25–45. Scopus, doi:10.1007/s10115-010-0313-0.
Huang K, Ying Y, Campbell C. Generalized sparse metric learning with relative comparisons. Knowledge and Information Systems. 2011 Jan 1;28(1):25–45.
Journal cover image

Published In

Knowledge and Information Systems

DOI

EISSN

0219-3116

ISSN

0219-1377

Publication Date

January 1, 2011

Volume

28

Issue

1

Start / End Page

25 / 45

Related Subject Headings

  • Information Systems
  • 46 Information and computing sciences
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing