Manifold Regularized Transfer Distance Metric Learning

Haibo Shi, Yong Luo, Chao Xu and Yonggang Wen

Abstract

The performance of many computer vision and machine learning algorithms are heavily depend on the distance metric between samples. It is necessary to exploit abundant of side information like pairwise constraints to learn a robust and reliable distance metric. While in real world application, large quantities of labeled data is unavailable due to the high labeling cost. Transfer distance metric learning (TDML) can be utilized to tackle this problem by leveraging different but certain related source tasks to learn a target metric. The recently proposed decomposition based TDML (DTDML) is superior to other TDML methods in that much fewer variables need to be learned. In spite of this success, the learning of the combination coefficients in DTDML still relies on the limited labeled data in the target task, and the large amounts of unlabeled data that are typically available are discarded. To utilize both the information contained in the source tasks, as well as the unlabeled data in the target task, we introduce manifold regularization in DTDML and develop the manifold regularized transfer distance metric learning (MTDML). In particular, the target metric in MTDML is learned to be close to an integration of the source metrics under the manifold regularization theme. That is, the target metric is smoothed along each data manifold that is approximated by all the labeled and unlabeled data in the target task and each source metric. In this way, more reliable target metric could be obtained given the limited labeled data in the target task. Extensive experiments on the NUS-WIDE and USPS dataset demonstrate the effectiveness of the proposed method.

Session

Poster 2

Files

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DOI

10.5244/C.29.158
https://dx.doi.org/10.5244/C.29.158

Citation

Haibo Shi, Yong Luo, Chao Xu and Yonggang Wen. Manifold Regularized Transfer Distance Metric Learning. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 158.1-158.11. BMVA Press, September 2015.

Bibtex

@inproceedings{BMVC2015_158,
	title={Manifold Regularized Transfer Distance Metric Learning},
	author={Haibo Shi and Yong Luo and Chao Xu and Yonggang Wen},
	year={2015},
	month={September},
	pages={158.1-158.11},
	articleno={158},
	numpages={11},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.158},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.158}
}