Abstract
Distance metric learning plays an important role in many machine learning tasks. In this paper, we propose a method for learning a Mahanalobis distance metric. By formulating the metric learning problem with relative distance constraints, we suggest a Relative Distance Constrained Metric Learning (RDCML) model which can be easily implemented and effectively solved by a modified support vector machine (SVM) approach. Experimental results on UCI datasets and handwritten digits datasets show that RDCML achieves better or comparable classification accuracy when compared with the state-of-the-art metric learning methods.
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Luo, C., Li, M., Zhang, H., Wang, F., Zhang, D., Zuo, W. (2015). Metric Learning with Relative Distance Constraints: A Modified SVM Approach. In: Wang, H., et al. Intelligent Computation in Big Data Era. ICYCSEE 2015. Communications in Computer and Information Science, vol 503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46248-5_30
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DOI: https://doi.org/10.1007/978-3-662-46248-5_30
Publisher Name: Springer, Berlin, Heidelberg
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