VIS Full Papers: That's way too many dimensions for me

Session chair:Dominik Moritz

Room:Essex North

Time:Thursday, Nov 12 @ 10:00 - 11:30 (US/Eastern)

Presentations in this session

10:00 - 10:12

When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting

Authors: Diede van der Hoorn, Alessio Arleo, Fernando Paulovich

10:12 - 10:24

Homology-Preserving Dimensionality Reduction via Adaptive Mapper and Landmark Isomap

Authors: Shakiba Khourashahi, Ilia Jahanshahi, Bei Wang, Lin Yan

10:24 - 10:36

MAPLE: Self-supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis

Authors: Zeyang Huang, Takanori Fujiwara, Angelos Chatzimparmpas, Wandrille Duchemin, Andreas Kerren

10:36 - 10:48

Towards More Explainable Nonlinear Dimensionality Reduction: A Feature-Driven Interaction Approach

Authors: Aeri Cho; Hyeon Jeon; Kiroong Choe; Seokhyeon Park; Jinwook Seo

10:48 - 11:00

Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality Reduction

Authors: Mattéo Clémot, Julie Digne, Julien Tierny

11:00 - 11:12

UMATO: Bridging Local and Global Structures for Reliable Visual Analytics with Dimensionality Reduction

Authors: Hyeon Jeon, Kwon Ko, Soohyun Lee, Jake Hyun, Taehyun Yang, Gyehun Go, Jaemin Jo, Jinwook Seo

11:12 - 11:24

Into the Void: Mapping the Unseen Gaps in High Dimensional Data

Authors: Xinyu Zhang, Tyler Estro, Geoff Kuenning, Erez Zadok, Klaus Mueller