TY - JOUR
T1 - Integrating Constraints into Dimensionality Reduction for Visualization
T2 - A Survey
AU - Vu, Viet Minh
AU - Bibal, Adrien
AU - Frénay, Benoît
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - 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.
AB - 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.
KW - Constraint integration
KW - dimensionality reduction (DR)
KW - visualization
UR - http://www.scopus.com/inward/record.url?scp=85137858479&partnerID=8YFLogxK
U2 - 10.1109/tai.2022.3204734
DO - 10.1109/tai.2022.3204734
M3 - Article
AN - SCOPUS:85137858479
SN - 2691-4581
VL - 3
SP - 944
EP - 962
JO - IEEE Transactions on Artificial Intelligence
JF - IEEE Transactions on Artificial Intelligence
IS - 6
ER -