calcTrustworthinessFromDist: Calculate trustworthiness based on distance matrices

View source: R/trustworthiness.R

calcTrustworthinessFromDistR Documentation

Calculate trustworthiness based on distance matrices

Description

The trustworthiness was proposed by Venna and Kaski, as a local quality measure of a low-dimensional representation. The metric focuses on the preservation of local neighborhoods, and compares the neighborhoods of points in the low-dimensional representation to those in the reference data. Hence, the trustworthiness measure indicates to which degree we can trust that the points placed closest to a given sample in the low-dimensional representation are really close to the sample also in the reference data set. The kTM parameter defines the size of the neighborhoods to consider.

Usage

calcTrustworthinessFromDist(distReference, distLowDim, kTM)

Arguments

distReference

N x N matrix or dist object, representing pairwise sample distances based on the reference (high-dimensional) observed values. For each column, samples (rows) will be ranked by the provided distances.

kTM

The number of nearest neighbors (excluding the sample itself).

rankLowDim

N x N matrix or dist object, representing pairwise sample distances based on the low-dimensional representation. For each column, samples (rows) will be ranked by the provided distances.

Value

The trustworthiness value.

Author(s)

Charlotte Soneson

References

Venna J., Kaski S. (2001). Neighborhood preservation in nonlinear projection methods: An experimental study. In Dorffner G., Bischof H., Hornik K., editors, Proceedings of ICANN 2001, pp 485–491. Springer, Berlin.


csoneson/dreval documentation built on Oct. 22, 2022, 12:56 p.m.