  Multidimensional projections (MP) are among the most essential approaches in
the visual analysis of multidimensional data. It transforms multidimensional
data into two-dimensional representations that may be shown as scatter plots
while preserving their similarity with the original data. Human visual
perception is frequently used to evaluate the quality of MP. In this work, we
propose to study and improve on a well-known map called Local Affine
Multidimensional Projection (LAMP), which takes a multidimensional instance and
embeds it in Cartesian space via moving least squares deformation. We propose a
new visual quality metric based on human perception. The new metric combines
three previously used metrics: silhouette coefficient, neighborhood
preservation, and silhouette ratio. We show that the proposed metric produces
more precise results in analyzing the quality of MP than other previously used
metrics. Finally, we describe an algorithm that attempts to overcome a
limitation of the LAMP method which requires a similar scale for control points
and their counterparts in the Cartesian space.
