The neighborhood counting measure (NCM) is a similarity measure based on the counting of all common neighborhoods in a data space. The minimum risk metric (MRM) is a distance measure based on the minimization of the risk of misclassification. The paper by Argentini and Blanzieri refutes a remark about the time complexity of MRM, and presents an experimental comparison of MRM and NCM. This paper addresses the questions raised by Argentini and Blanzieri. The original remark is clarified by a combination of theoretical analysis of different implementations of MRM and experimental comparison of MRM and NCM using straightforward implementations of the two measures.
|Journal||IEEE Transactions on Pattern Analysis and Machine Intelligence|
|Publication status||Published (in print/issue) - Apr 2010|