Comparing distance metrics for rotation using the k-nearest neighbors algorithm for entropy estimation.

ABSTRACT: Distance metrics facilitate a number of methods for statistical analysis. For statistical mechanical applications, it is useful to be able to compute the distance between two different orientations of a molecule. However, a number of distance metrics for rotation have been employed, and in this study, we consider different distance metrics and their utility in entropy estimation using the k-nearest neighbors (KNN) algorithm. This approach shows a number of advantages over entropy estimation using a histogram method, and the different approaches are assessed using uniform randomly generated data, biased randomly generated data, and data from a molecular dynamics (MD) simulation of bulk water. The results identify quaternion metrics as superior to a metric based on the Euler angles. However,

SUBMITTER: Huggins DJ 

PROVIDER: S-EPMC4238811 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature