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Guided Stable Dynamic Projections
Author(s) -
Vernier E. F.,
Comba J. L. D.,
Telea A. C.
Publication year - 2021
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.14291
Subject(s) - computer science , benchmark (surveying) , representation (politics) , projection (relational algebra) , stability (learning theory) , similarity (geometry) , encode , artificial intelligence , data mining , algorithm , computer vision , machine learning , image (mathematics) , biochemistry , chemistry , geodesy , politics , political science , law , gene , geography
Projections aim to convey the relationships and similarity of high‐dimensional data in a low‐dimensional representation. Most such techniques are designed for static data. When used for time‐dependent data, they usually fail to create a stable and suitable low dimensional representation. We propose two dynamic projection methods (PCD‐tSNE and LD‐tSNE) that use global guides to steer projection points. This avoids unstable movement that does not encode data dynamics while keeping t‐SNE's neighborhood preservation ability. PCD‐tSNE scores a good balance between stability, neighborhood preservation, and distance preservation, while LD‐tSNE allows creating stable and customizable projections. We compare our methods to 11 other techniques using quality metrics and datasets provided by a recent benchmark for dynamic projections.