calcTrustworthinessFromRank: Calculate trustworthiness based on sample rankings

View source: R/trustworthiness.R

calcTrustworthinessFromRankR Documentation

Calculate trustworthiness based on sample rankings

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

calcTrustworthinessFromRank(rankReference, rankLowDim, kTM)

Arguments

rankReference

N x N matrix, each row/column corresponding to one sample. The value of entry (i, j) represents the position of sample i in the ranking of all samples with respect to their distance from sample j, based on the reference (high-dimensional) observed values. The sample itself has rank 0.

rankLowDim

N x N matrix, each row/column corresponding to one sample. The value of entry (i, j) represents the position of sample i in the ranking of all samples with respect to their distance from sample j, based on the low-dimensional representation. The sample itself has rank 0.

kTM

The number of nearest neighbors.

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.