A Review, Framework and R toolkit for Exploring, Evaluating, and Comparing Visualizations
This work provides a framework and tools for researchers in data science and machine learning to better evaluate dimensionality reduction methods, though it is incremental as it synthesizes existing metrics and adds visualization capabilities.
The paper tackles the problem of evaluating and comparing dimensionality reduction techniques by reviewing existing methods, creating a framework for visualization-based quality assessment, and implementing an R toolkit with various plotting features. The result is a practical resource that helps researchers select and improve techniques through visual insights, demonstrated with examples on manifolds and a consumer survey dataset.
This paper gives a review and synthesis of methods of evaluating dimensionality reduction techniques. Particular attention is paid to rank-order neighborhood evaluation metrics. A framework is created for exploring dimensionality reduction quality through visualization. An associated toolkit is implemented in R. The toolkit includes scatter plots, heat maps, loess smoothing, and performance lift diagrams. The overall rationale is to help researchers compare dimensionality reduction techniques and use visual insights to help select and improve techniques. Examples are given for dimensionality reduction of manifolds and for the dimensionality reduction applied to a consumer survey dataset.