HUMAP: Hierarchical Uniform Manifold Approximation and Projection
São Paulo State University (UNESP) and Nomic AI（圣保罗州立大学（UNESP）和Nomic AI）；São Paulo State University (UNESP)（圣保罗州立大学（UNESP））；Eindhoven University of Technology（埃因霍温理工大学）；Linnaeus University（林纳大学）
Wilson E. Marcílio-Jr, Danilo M. Eler, Fernando V. Paulovich, Rafael M. Martins
Dimensionality reduction (DR) techniques help analysts to understand patterns in high-dimensional spaces. These techniques, often represented by scatter plots, are employed in diverse science domains and facilitate similarity analysis among clusters and data samples. For datasets containing many granularities or when analysis follows the information visualization mantra, hierarchical DR techniques are the most suitable approach since they present major structures beforehand and details on demand. This work presents HUMAP, a novel hierarchical dimensionality reduction technique designed to be flexible on preserving local and global structures and preserve the mental map throughout hierarchical exploration. We provide empirical evidence of our technique's superiority compared with current hierarchical approaches and show a case study applying HUMAP for dataset labelling.
Robust Principal Component Analysis via Discriminant Sample Weight Learning
Harbin Engineering University（哈尔滨工程大学）；College of Intelligent System Science and Engineering, Harbin Engineering University（哈尔滨工程大学智能科学与工程学院）；University of Southampton（南安普顿大学）；School of Engineering, University of Southampton（南安普顿大学工程学院）
Scalable Graph Attention-based Instance Selection via Mini-Batch Sampling and Hierarchical Hashing
College of Information Technology, United Arab Emirates University（阿联酋大学信息科技学院）；Department of Computer Science, KU Leuven（比利时库尔勒大学计算机科学系）；College of Science, United Arab Emirates University（阿联酋大学科学学院）
