View source: R/trustworthiness.R
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
parameter defines the size of the neighborhoods to consider.
calcTrustworthinessFromDist(distReference, distLowDim, kTM)
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.
The number of nearest neighbors (excluding the sample itself).
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.
The trustworthiness value.
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.
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