Guided Stable Dynamic Projections
| dc.contributor.author | Vernier, Eduardo Faccin | en_US |
| dc.contributor.author | Comba, João L. D. | en_US |
| dc.contributor.author | Telea, Alexandru C. | en_US |
| dc.contributor.editor | Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von | en_US |
| dc.date.accessioned | 2021-06-12T11:01:23Z | |
| dc.date.available | 2021-06-12T11:01:23Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | 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. | en_US |
| dc.description.number | 3 | |
| dc.description.sectionheaders | Multivariate Data and Dimension Reduction | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 40 | |
| dc.identifier.doi | 10.1111/cgf.14291 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 87-98 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.14291 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf14291 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.subject | Computing methodologies | |
| dc.subject | Dimensionality reduction and manifold learning | |
| dc.title | Guided Stable Dynamic Projections | en_US |