Generalized sparse metric learning with relative comparisons

Research output: Contribution to journalArticle (Academic Journal)peer-review

26 Citations (Scopus)

Abstract

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.
Translated title of the contributionGeneralized sparse metric learning with relative comparisons
Original languageEnglish
Pages (from-to)25 - 45
Number of pages20
JournalKnowledge and Information Systems
Volume28
DOIs
Publication statusPublished - Jun 2011

Bibliographical note

Author of Publication Reviewed: Kaizhu Huang, Yiming Ying and Colin Campbell
Publisher: Springer

Fingerprint Dive into the research topics of 'Generalized sparse metric learning with relative comparisons'. Together they form a unique fingerprint.

Cite this