Conditional Multidimensional Scaling with Incomplete Conditioning Data
This addresses a limitation in data analysis for researchers and practitioners by enabling conditional scaling with incomplete data, though it is incremental as it extends existing methods to handle missingness.
The paper tackles the problem of performing conditional multidimensional scaling when conditioning data have missing values, proposing a method that learns the low-dimensional configuration and imputes missing values, with implementation available in an R package.
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.