In this paper, the projection evaluation measures such as stress function, Spearman’s rho, Konig’s topology preservation, silhouette and Renyi entropy have been analyzed. The principal component analysis (PCA) and part–linear multidimensional projection (PLMP) techniques are used to reduce the dimensionality of the initial data set. The experiments have been carried out with seven real and artificial datasets. The experimental investigation has shown that several quality evaluation
measures have to be used when dimension reduction problem is solved.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Please read the Copyright Notice in Journal Policy.