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.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.