Integrating Constraints Into Dimensionality Reduction for Visualization: A Survey
Viet Minh Vu, Adrien Bibal, Benoît Frénay · IEEE Transactions on Artificial Intelligence · 2022
This survey reviews and organizes existing methods for integrating constraints into dimensionality reduction (DR). In the world of high-dimensional data, DR methods help to reduce dimensionality while preserving important structures to facilitate subsequent tasks, such as data visualization. While DR methods only reveal hidden structures from the original data, additional information, such as class labels, external features, or even feedback or prior knowledge from users can help to enrich low-dimensional representations. We consider all these types of additional information as constraints. Integrating constraints into classification and clustering methods is well studied, yet, a systematic review on constraint integration in DR methods for visualization is still lacking. We contribute to the literature of constraints in DR visualizations with a novel categorization focusing on constraint types. This survey also introduces new perspectives on the subject, and suggests new trends and future research directions for combining constraints and DR methods.