Abstract
In this article we introduce new multidimensional scaling variants: sparsified multidimensional scaling (SMDS), sparsified power multidimensional scaling (SPMDS), sparsified multidimensional distance analysis (SMDDA), sparsified power multidimensional distance analysis (SPMDDA). These methods are inspired by the idea of curvilinear component analysis and weight the approximation error with a heaviside function to ignore larger fitted distances in the configuration, thus effectively providing a localized version of multidimensional scaling. Sparsified refers to the weight matrix being sparse. We estimate the models with a quasi-majorization algorithm.
| Original language | English |
|---|---|
| Publisher | WU Vienna University of Economics and Business |
| DOIs | |
| Publication status | Published - 2024 |
Publication series
| Series | Discussion Paper Series / Center for Empirical Research Methods |
|---|---|
| Number | 2024/01 |
WU Working Papes and Cases
- Discussion Paper Series / Center for Empirical Research Methods
Keywords
- proximity scaling
- dimensionality reduction
- multivariate statistics
- manifold learning
- data visualization
- ordination
Other versions
- 1 Journal article
-
Multidimensional Scaling With Heaviside Weighting: Extensions to Curvilinear Component Analysis and Curvilinear Distance Analysis
Rusch, T., 2025, In: Stat. 14, 3, p. 1-11 11 p., e70086.Publication: Scientific journal › Journal article › peer-review
Open Access
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