What are some common challenges and pitfalls of data visualization with dimensionality reduction?

1 What is dimensionality reduction?

Dimensionality reduction is a process of transforming a high-dimensional data set into a lower-dimensional one, while preserving as much of the relevant information as possible. This can help reduce noise, redundancy, and computational cost, as well as enable visualization of the data in two or three dimensions. Some of the common dimensionality reduction techniques are principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP).

2 Why use dimensionality reduction for visualization?

Visualization is a useful tool for exploring, analyzing, and communicating data, especially when it is complex and high-dimensional. However, human perception and cognition are limited by the number of dimensions that can be easily perceived and processed. Therefore, dimensionality reduction can help overcome this limitation by reducing the complexity and dimensionality of the data, and revealing the underlying structure and patterns that are otherwise hidden or obscured. Dimensionality reduction can also help identify outliers, clusters, trends, and correlations in the data, as well as facilitate comparison and interpretation.

3 What are some challenges of dimensionality reduction for visualization?

Despite its advantages, dimensionality reduction for visualization also comes with some challenges that need to be addressed and understood. It is important to choose the right technique, as different techniques have different assumptions, objectives, and properties, and they may produce different results. Additionally, interpreting the results requires caution and context, as it is a process of approximation and trade-off where some information is inevitably lost or distorted. Lastly, evaluating the quality of the dimensionality reduction for visualization should consider multiple aspects such as how well it preserves relevant information, reveals interesting patterns, avoids misleading artifacts, and supports exploration, analysis, and communication of the data.

4 How to avoid or overcome the pitfalls of dimensionality reduction for visualization?

To avoid or overcome the pitfalls of dimensionality reduction for visualization, there are some tips and best practices to follow. It is important to have a good understanding of your data, such as its type, distribution, dimensionality, variability, and quality. Additionally, you should have a good understanding of the technique’s assumptions, objectives, and properties. Furthermore, you should have a clear goal and question that you want to answer or explore with your data. Finally, you should have an idea of your audience and what they need or expect from your visualization. By following these tips and best practices, you can choose the most suitable technique and parameters for your data and create an informative visualization tailored to your audience.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?