Sentence examples for multidimensional scaling technique from inspiring English sources

The standard methodology used in these studies was based on a multidimensional scaling technique (MDS) applied to dissimilarity ratings.

Secondly, ALSCAL algorithm of the multidimensional scaling technique was run to explore the patterns of cognitive abilities in programming performance groups.

We utilize the weighted metric multidimensional scaling technique to define the potential function in the A∗ algorithm by converting the link travel costs (times) into distances.

The highest quality results are provided by the catchment similarity approach in which a single donor catchment was selected in accordance with an optimum catchment similarity index computed by the multidimensional scaling technique.

We observed a similar dynamics by analysing unilineage HPCs differentiation using a multidimensional scaling technique: the distances between different cell lines, while increasing in time, maintain their mutual correlation/orientation in the transcriptome space.

Similarity data were collected by way of a paired comparison task, and multidimensional scaling techniques were used to map these data into spatial representations.

Finally, principal component regression (PCR) [ 31] represents the class of multidimensional scaling techniques.

Data subgroups and extreme dimensions of SNP array data may be checked by multidimensional scaling techniques.

This way, the multidimensional scaling techniques are used in information visualization for exploring similarities or dissimilarities in data.

Many algorithms have been proposed in the literature and, among them, the Multidimensional Scaling (MDS) technique gained a lot of interest due to its resilience to flips ambiguities and easiness of use.

Two key ideas are addressed in this study: (i) the use of stochastic simulations to generate a local cumulative distribution function or extreme classes of each attribute and (ii) the use of a multidimensional scaling (MDS) technique to visualize and quantify the relative importance of each attribute in the classification process.