A dual distance metrics method for improving classification performance. Issue 1 (31st December 2020) - Wu, Lian; Xu, Yong; Zhao, Yong; Hu, Zhijun; Sun, Lilei
Abstract: Distance metric forms the basis of pattern classification, as almost all classifiers depend on such a metric for making classification decisions. However, the existing research testifies that a single‐distance metric is not robust enough for classification. In this Letter, the authors propose a dual distance metrics method and modify collaborative representation using dual distance metrics, that is, collaborative representation related class distance and conventional sample distance. These two distance metrics are fused by a parameter‐free multiplication scheme. The rationale of the designed multiplication fusion can be interpreted from the viewpoint of probability. As the multiplication fusion exploit the two distance metrics effectively and allow the best of the distance metrics to dominate the final classification decision, the improved collaborative representation based on dual distance metric can achieve a very high accuracy rate. Experimental results show that the proposed method has promising performance. It is also noted that the values of the two distance metrics vary with the test sample, therefore, the two distance metrics play different roles for different test samples. In other words, the two distance metrics are adaptive for test samples. The idea of the dual distance metrics method is also suitable for other combinations of other distance metrics.