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Visualization of cubic (one-way, three-mode) dissimilarity data by using multidimensional scaling

Yadohisa H., Yamamoto Y.

Machine Graphics and Vision

2000

Vol. 9, No. 1/2

243-249

A visualization method for cubic (one-way, three-mode) dissimilarity data is proposed. By using the framework of multidimensional scaling (MDS), the data are representated as points in a euclidean space. Two model to explain the data are proposed, and estimates are made by the alternating least squares method.

Graphical representation of asymmetry in three-way dissimilarity data

Yadohisa H.

Machine Graphics and Vision

1999

Vol. 8, No. 2

265-279

Vector models for representation of asymmetry in three-way (dis)similarity data are proposed. We evaluate several different data matrices corresponding to observations, individuals and so on. We then propose models for representation of asymmerty on the basis of the INDSCAL (Carroll and Chang, 1970) and GEMSCAL (Young, 1984) models.

Visualization of proximity data by multidimensional scaling with residual from space reduction

Yadohisa H.

Machine Graphics and Vision

1998

Vol. 7, No. 3

681-686

New multidimensional scaling method for representing a two-way proximity data is proposed. Given an n x n data matrix D of proximity measures between n objects, a configuration of objects, optimum in a sense, is detemined by applying some suitable multidimensional scaling method as a set of coordinates X. A residual matrix composed from X. The main purpose of this paper is to propose a graphical method for representing the residual caused space reduction.