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Conditional multidimensional scaling with incomplete conditioning data
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DOI：10.1016/j.jmva.2026.105620.png)
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Abstract
En 中文
Conditional multidimensional scaling seeks for a low-dimensional configuration from pairwise dissimilarities, in the presence of other known features. By taking advantage of available data of the known features, conditional multidimensional scaling improves the estimation quality of the low-dimensional configuration and simplifies knowledge discovery tasks. However, existing conditional multidimensional scaling methods require full data of the known features, which may not be always attainable due to time, cost, and other constraints. This paper proposes a conditional multidimensional scaling method that can learn the low-dimensional configuration when there are missing values in the known features. The method can also impute the missing values, which provides additional insights of the problem. Computer codes of this method are maintained in the cml R package on CRAN.
Keywords:
Dimension reduction
Distance scaling
ISOMAP
Manifold learning
Missing data
Sammon mapping
Journal
J
IF:
1.7
Papers:
97
Citations:
5.8K