Article ID Journal Published Year Pages File Type
535438 Pattern Recognition Letters 2014 8 Pages PDF
Abstract

•Computation of vertices of hyperspheres.•Deriving theoretical maximum relative error (MRE).•Reporting new distances as good approximators of Euclidean norms.

Previously, we have studied linear combinations of a few pairs of norms, and reported their effectiveness in providing better approximation of Euclidean norms. In particular, we showed good approximation property of a combination of a pair of norms, namely CWDeuCWDeu and WtDisrWtDisr by experimentally computing their approximate maximum relative errors (MRE) with respect to the Euclidean norm. In this work, we have considered a pairing of any two members from the families of chamfering weighted distances (CWD) and weighted t  -cost distances (WtD), respectively, and derive theoretical values of MREs with respect to the Euclidean norm by exploiting geometry of its hypersphere. Towards this we have computed the vertices of the hypersphere. Subsequently, in addition to our previously reported combination of CWDeuCWDeu and WtDisrWtDisr, we have also considered a few other combinations and showed their good approximation properties by computing theoretical MREs, as well as by validating those values experimentally. Further, by minimizing the theoretical expressions of MRE locally in the coefficient space of a linear combination, we obtain good approximators of Euclidean norm in any arbitrary dimension.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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