# A Review, Framework and R toolkit for Exploring, Evaluating, and Comparing Visualizations

## TL;DR

This paper reviews evaluation methods for dimensionality reduction, introduces a visualization framework, and provides an R toolkit to help researchers compare and improve these techniques using visual insights.

## Contribution

It presents a comprehensive review, a new visualization framework, and an R toolkit for evaluating and comparing dimensionality reduction methods.

## Key findings

- The toolkit enables effective comparison of dimensionality reduction techniques.
- Visualizations help identify the quality of manifold embeddings.
- Examples demonstrate practical application to survey data.

## Abstract

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.