That's way too many dimensions for me
Session chair:Dominik Moritz
Presentations in this session
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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
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Homology-Preserving Dimensionality Reduction via Adaptive Mapper and Landmark Isomap
Authors: Shakiba Khourashahi, Ilia Jahanshahi, Bei Wang, Lin Yan
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MAPLE: Self-supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
Authors: Zeyang Huang, Takanori Fujiwara, Angelos Chatzimparmpas, Wandrille Duchemin, Andreas Kerren
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Toward More Explainable Nonlinear Dimensionality Reduction: A Feature-Driven Interaction Approach
Authors: Aeri Cho, Hyeon Jeon, Kiroong Choe, Seokhyeon Park, Jinwook Seo
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Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality Reduction
Authors: Mattéo Clémot, Julie Digne, Julien Tierny
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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
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Into the Void: Mapping the Unseen Gaps in High Dimensional Data
Authors: Xinyu Zhang, Tyler Estro, Geoff Kuenning, Erez Zadok, Klaus Mueller