Standard multidimensional scaling takes as input a dissimilarity matrix of general term $\delta _{ij}$ which is a numerical value. In this paper we input $\delta _{ij}=[\underline{\delta _{ij}},\overline{\delta _{ij}}]$ where $\underline{\delta _{ij}}$ and $\overline{\delta _{ij}}$ 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.

翻译：标准多维缩放以一般项$\delta _{ij}$的相异度矩阵作为输入，其中$\delta _{ij}$为数值。本文输入$\delta _{ij}=[\underline{\delta _{ij}},\overline{\delta _{ij}}]$，其中$\underline{\delta _{ij}}$和$\overline{\delta _{ij}}$分别为刺激/对象$S_i$与刺激/对象$S_j$间"相异度"的下界和上界。与其它多维缩放方法在因子平面上用点表示每个刺激/对象不同，该方法以矩形形式可视化每个刺激/对象，以反映相异度的变异。我们对经典缩放方法进行推广，旨在产生与Tops主成分分析类似的结果。通过两个实例验证了所提方法的有效性。