Visual Analytics for Dimension Reduction Algorithms
Problem
Dimension Reduction (DR) Algorithms are used to analyze multivariate tabular data, such as the oftenused car dataset (https://www.kaggle.com/toramky/automobiledataset). They often aim to find a 2dimensional projection for pvariate tabular data such that the proximity in 2D space encodes similarity in the original space, ie. they find 2 synthetic dimensions. A popular example is principal component analysis (PCA), and its synthetic dimensions are those with the largest variance. PCA is a linear DR algorithm, as its dimensions are a linear combination of the original dimensions, but nonlinear algorithms such as UMAP or tSNE exist. For many of these one needs to set (hyper)parameters that influence the result greatly. When people need to explain them, the synthetic dimensions themselves are of interest.
Aim
Provide a systematic overview of VA research in dimension reduction. Possible focal points (not exhaustive) are how application prototypes link the result to parameters, or how they support explanation of synthetic dimensions.
Other information
References

D. H. Jeong, C. Ziemkiewicz, B. Fisher, W. Ribarsky, and R. Chang, “iPCA: An Interactive System for PCAbased Visual Analytics,” Computer Graphics Forum, vol. 28, no. 3, pp. 767–774, 2009, doi: 10.1111/j.14678659.2009.01475.x.

M. Gleicher, “Explainers: Expert Explorations with Crafted Projections,” IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 2042–2051, Dec. 2013, doi: 10.1109/TVCG.2013.157.

M. Espadoto, R. M. Martins, A. Kerren, N. S. T. Hirata, and A. C. Telea, “Towards a Quantitative Survey of Dimension Reduction Techniques,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–1, 2019, doi: 10.1109/TVCG.2019.2944182.

L. G. Nonato and M. Aupetit, “Multidimensional Projection for Visual Analytics: Linking Techniques with Distortions, Tasks, and Layout Enrichment,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 8, pp. 2650–2673, Aug. 2019, doi: 10.1109/TVCG.2018.2846735.