Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements.
Inverse MDS: Inferring Dissimilarity Structure from Multiple Item Arrangements.
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DOI：
10.3389/fpsyg.2012.00245
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发表时间：
2012
影响因子：
3.8
通讯作者：
Mur M
中科院分区：
文献类型：
--
作者：
Kriegeskorte N;Mur M
一组项目的成对差异可以通过项目的二维排列直观地可视化，其中距离反映了差异。这种排列可以通过多维尺度（MDS）来实现。我们提出了一种逆过程的方法：从多个二维项目安排中推断成对的不相似性。知觉差异通常是用两两不相似判断来测量的。然而，以前已经提出了其他方法，包括自由分类和二维排列。该方法的新颖之处在于：(a)基于项目子集的多重排列，通过“逆MDS”估计不相似矩阵；(b)子集采用自适应算法设计，旨在为不相似估计提供最优证据。受试者通过鼠标拖放操作来排列项目（在计算机屏幕上表示为图标）。多重排列方法可以解释为简单方法的概括：如果每个排列只包含两个项目，则可以简化为成对不相似判断，如果项目被分类地排列成离散的堆，则可以简化为自由排序。多重排列结合了这些方法的优点。它是有效的（因为受试者通过每次鼠标拖动传达许多不同的判断），心理上有吸引力（因为不同是在上下文中判断的），并且可以表征连续的高维不同结构。我们提出了估计不相似矩阵的两种方法：部分不相似矩阵的简单加权对齐平均和计算密集型算法，该算法通过迭代最小化受试者排列的mds预测误差来估计不相似矩阵。交互式排列和不相似度估计的Matlab代码可根据需要从作者处获得。
The pairwise dissimilarities of a set of items can be intuitively visualized by a 2D arrangement of the items, in which the distances reflect the dissimilarities. Such an arrangement can be obtained by multidimensional scaling (MDS). We propose a method for the inverse process: inferring the pairwise dissimilarities from multiple 2D arrangements of items. Perceptual dissimilarities are classically measured using pairwise dissimilarity judgments. However, alternative methods including free sorting and 2D arrangements have previously been proposed. The present proposal is novel (a) in that the dissimilarity matrix is estimated by “inverse MDS” based on multiple arrangements of item subsets, and (b) in that the subsets are designed by an adaptive algorithm that aims to provide optimal evidence for the dissimilarity estimates. The subject arranges the items (represented as icons on a computer screen) by means of mouse drag-and-drop operations. The multi-arrangement method can be construed as a generalization of simpler methods: It reduces to pairwise dissimilarity judgments if each arrangement contains only two items, and to free sorting if the items are categorically arranged into discrete piles. Multi-arrangement combines the advantages of these methods. It is efficient (because the subject communicates many dissimilarity judgments with each mouse drag), psychologically attractive (because dissimilarities are judged in context), and can characterize continuous high-dimensional dissimilarity structures. We present two procedures for estimating the dissimilarity matrix: a simple weighted-aligned-average of the partial dissimilarity matrices and a computationally intensive algorithm, which estimates the dissimilarity matrix by iteratively minimizing the error of MDS-predictions of the subject’s arrangements. The Matlab code for interactive arrangement and dissimilarity estimation is available from the authors upon request.
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影响因子：
5.3
作者：
Risvik, E.;McEwan, J. A.;Lyon, D. H.
通讯作者：
Lyon, D. H.
影响因子：
16.2
作者：
Kriegeskorte N;Mur M;Ruff DA;Kiani R;Bodurka J;Esteky H;Tanaka K;Bandettini PA
通讯作者：
Bandettini PA
影响因子：
3
作者：
SHEPARD, RN
通讯作者：
SHEPARD, RN
影响因子：
2
作者：
deLeeuw, J;Groenen, PJF
通讯作者：
Groenen, PJF
影响因子：
3
作者：
TORGERSON, WS
通讯作者：
TORGERSON, WS