Year: 2024
Type: preprint
Abstract: Standard multidimensional scaling takes as input a dissimilarity matrix of general term $δ_{ij}$ which is a numerical value. In this paper we input $δ_{ij}=[\underline{δ_{ij}},\overline{δ_{ij}}]$ where $\underline{δ_{ij}}$ and $\overline{δ_{ij}}$ are the lower bound and the upper bound of the ``dissimilarity'' between the stimulus/object $S_i$ and the stimulus/object $S_j$ respectively. As output instead of representing each stimulus/object on a factorial plane by a point, as in other multidimensional scaling methods, in the proposed method each stimulus/object is visualized by a rectangle, in order to represent dissimilarity variation. We generalize the classical scaling method looking for a method that produces results similar to those obtained by Tops Principal Components Analysis. Two examples are presented to illustrate the effectiveness of the proposed method.
Source: arXiv (Cornell University)
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4390832939