Multidimensional Scaling of Asymmetric Distance Matrices

Multidimensional Scaling of Asymmetric Distance Matrices
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초록

In most cases of multidimensional scaling(MDS), the distances or dissimilarities among units are assumed to be symmetric. Thus, it is not an easy task to deal with asymmetric distances. Asymmetric MDS developed so far face difficulties in the interpretation of results. This study proposes a much simpler asymmetric MDS, that utilizes the notion of ``altitude''. The analogy arises in mountaineering: It is easier (more difficult) to move from the higher (lower) point to the lower (higher). The idea is formulated as a quantification problem, in which the disparity of distances is maximally related to the altitude difference. The proposed method is demonstrated in three examples, in which the altitudes are visualized by rainbow colors to ease the interpretability of users.

키워드

Multidimensional scaling(MDS)similarityasymmetric distance matrixaltitude modelsocial network analysis.
제목
Multidimensional Scaling of Asymmetric Distance Matrices
제목 (타언어)
Multidimensional Scaling of Asymmetric Distance Matrices
저자
허명회이용구
발행일
2012
저널명
응용통계연구
25
4
페이지
613 ~ 620