That's way too many dimensions for me
Session chair:Dominik Moritz
Timezone
Presentations in this session
10:00 - 10:12
When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting
Authors: Diede P M van der Hoorn, Alessio Arleo, Fernando V 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
Toward 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