Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Huang, Zeyang, Fujiwara, Takanori, Chatzimparmpas, Angelos, Duchemin, Wandrille, Kerren, Andreas
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Human-Computer Interaction
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Spectral Framework for Multi-Scale Nonlinear Dimensionality Reduction
by: Huang, Zeyang, et al.
Published: (2026)

Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023
by: Chatzimparmpas, Angelos, et al.
Published: (2024)

MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels
by: Ploshchik, Ilya, et al.
Published: (2022)

HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques
by: Chatzimparmpas, Angelos, et al.
Published: (2022)

VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees
by: Chatzimparmpas, Angelos, et al.
Published: (2021)

t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
by: Chatzimparmpas, Angelos, et al.
Published: (2020)

VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
by: Chatzimparmpas, Angelos, et al.
Published: (2020)

FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
by: Chatzimparmpas, Angelos, et al.
Published: (2021)

DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps
by: Chatzimparmpas, Angelos, et al.
Published: (2023)

StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
by: Chatzimparmpas, Angelos, et al.
Published: (2020)

Visual Analytics for Explainable and Trustworthy Artificial Intelligence
by: Chatzimparmpas, Angelos
Published: (2025)

DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine
by: Salmanian, Parisa, et al.
Published: (2024)

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
by: Chatzimparmpas, A., et al.
Published: (2022)

Aiding Humans in Financial Fraud Decision Making: Toward an XAI-Visualization Framework
by: Chatzimparmpas, Angelos, et al.
Published: (2024)

Adversarial Attacks on Machine Learning-Aided Visualizations
by: Fujiwara, Takanori, et al.
Published: (2024)

LangLasso: Interactive Cluster Descriptions through LLM Explanation
by: Buchmüller, Raphael, et al.
Published: (2026)

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
by: Jeon, Hyeon, et al.
Published: (2025)

Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization
by: Long, Sheng, et al.
Published: (2025)

Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling
by: Zhang, Dongping, et al.
Published: (2024)

GhostUMAP2: Measuring and Analyzing (r,d)-Stability of UMAP
by: Jung, Myeongwon, et al.
Published: (2025)

Dataset-Adaptive Dimensionality Reduction
by: Jeon, Hyeon, et al.
Published: (2025)

Adaptive Crowdsourcing Via Self-Supervised Learning
by: Kagrecha, Anmol, et al.
Published: (2024)

Visual Analytics of Multivariate Networks with Representation Learning and Composite Variable Construction
by: Lu, Hsiao-Ying, et al.
Published: (2023)

Dimensionality Reduction Considered Harmful (Some of the Time)
by: Jeon, Hyeon
Published: (2025)

Understanding Bias in Perceiving Dimensionality Reduction Projections
by: Doh, Seoyoung, et al.
Published: (2025)

Visual Analytics Using Tensor Unified Linear Comparative Analysis
by: Okami, Naoki, et al.
Published: (2025)

Unveiling High-dimensional Backstage: A Survey for Reliable Visual Analytics with Dimensionality Reduction
by: Jeon, Hyeon, et al.
Published: (2025)

Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised Approaches
by: Sheng, Taoran, et al.
Published: (2025)

Uncovering Temporal Patterns in Visualizations of High-Dimensional Data
by: Poličar, Pavlin G., et al.
Published: (2024)

Predicting Driver's Perceived Risk: a Model Based on Semi-Supervised Learning Strategy
by: Huang, Siwei, et al.
Published: (2025)

VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region Annotation
by: Poličar, Pavlin G., et al.
Published: (2024)

GFlowState: Visualizing the Training of Generative Flow Networks Beyond the Reward
by: Holeczek, Florian, et al.
Published: (2026)

ClimateSOM: A Visual Analysis Workflow for Climate Ensemble Datasets
by: Kawakami, Yuya, et al.
Published: (2025)

MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
by: Lian, Zheng, et al.
Published: (2024)

Efficient Visual Appearance Optimization by Learning from Prior Preferences
by: Li, Zhipeng, et al.
Published: (2025)

A Systematic Evaluation of Self-Supervised Learning for Label-Efficient Sleep Staging with Wearable EEG
by: Estevan, Emilio, et al.
Published: (2025)

SmartAlert: Implementing Machine Learning-Driven Clinical Decision Support for Inpatient Lab Utilization Reduction
by: Liang, April S., et al.
Published: (2025)

ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems
by: Li, Harry, et al.
Published: (2023)

Perception-Driven Bias Detection in Machine Learning via Crowdsourced Visual Judgment
by: Tupakula, Chirudeep, et al.
Published: (2025)

Enhancing Uncertainty Communication in Time Series Predictions: Insights and Recommendations
by: Karagappa, Apoorva, et al.
Published: (2024)