Image de couverture de livre

Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	van der Hoorn, Diede P. M., Arleo, Alessio, Paulovich, Fernando V.
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting
par: van der Hoorn, Diede P. M., et autres
Publié: (2026)

Mind the Gaps: Measuring Visual Artifacts in Dimensionality Reduction
par: Ros, Jaume, et autres
Publié: (2025)

When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities
par: Paulovich, Fernando, et autres
Publié: (2024)

HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques
par: Chatzimparmpas, Angelos, et autres
Publié: (2022)

Why I Can't Create a Learning Center
par: Miller, Rosalind
Publié: (1975)

Probably Approximately Precision and Recall Learning
par: Cohen, Lee, et autres
Publié: (2024)

Precision and Recall Reject Curves for Classification
par: Fischer, Lydia, et autres
Publié: (2023)

Why Can't Transformers Learn Multiplication? Reverse-Engineering Reveals Long-Range Dependency Pitfalls
par: Bai, Xiaoyan, et autres
Publié: (2025)

Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization
par: Gao, Zhiqi, et autres
Publié: (2026)

Adaptive Experimentation When You Can't Experiment
par: Zhao, Yao, et autres
Publié: (2024)

Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein
par: Van Assel, Hugues, et autres
Publié: (2024)

Online Set Learning from Precision and Recall Feedback
par: Cohen, Lee, et autres
Publié: (2026)

I Can't Believe It's Not Robust: Catastrophic Collapse of Safety Classifiers under Embedding Drift
par: Sahoo, Subramanyam, et autres
Publié: (2026)

Ensemble of Precision-Recall Curve (PRC) Classification Trees with Autoencoders
par: Miao, Jiaju, et autres
Publié: (2025)

LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
par: Kambhampati, Subbarao, et autres
Publié: (2024)

HUMAP: Hierarchical Uniform Manifold Approximation and Projection
par: Marcílio-Jr, Wilson E., et autres
Publié: (2021)

Exploring Precision and Recall to assess the quality and diversity of LLMs
par: Bronnec, Florian Le, et autres
Publié: (2024)

Orthogonal Subspace Clustering: Enhancing High-Dimensional Data Analysis through Adaptive Dimensionality Reduction and Efficient Clustering
par: Wen, Qing-Yuan, et autres
Publié: (2026)

Learning-Augmented K-Means Clustering Using Dimensional Reduction
par: Jabari, Issam K. O, et autres
Publié: (2024)

I Can't Believe It's Not Real: CV-MuSeNet: Complex-Valued Multi-Signal Segmentation
par: Shin, Sangwon, et autres
Publié: (2025)

Why Tabular Foundation Models Should Be a Research Priority
par: van Breugel, Boris, et autres
Publié: (2024)

On the Geometry of Receiver Operating Characteristic and Precision-Recall Curves
par: Sameni, Reza
Publié: (2025)

You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning
par: Yang, Tianmeng, et autres
Publié: (2024)

Unifying and extending Precision Recall metrics for assessing generative models
par: Sykes, Benjamin, et autres
Publié: (2024)

On the Use of Relative Validity Indices for Comparing Clustering Approaches
par: Yerbury, Luke W., et autres
Publié: (2024)

FullRecall: A Semantic Search-Based Ranking Approach for Maximizing Recall in Patent Retrieval
par: Ali, Amna, et autres
Publié: (2025)

Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models
par: Park, Dogyun, et autres
Publié: (2023)

Climbing the Ladder of Reasoning: What LLMs Can-and Still Can't-Solve after SFT?
par: Sun, Yiyou, et autres
Publié: (2025)

The Struggle You Can’t See
par: Lierman, Ash
Publié: (2024)

What Is the Optimal Ranking Score Between Precision and Recall? We Can Always Find It and It Is Rarely $F_1$
par: Piérard, Sébastien, et autres
Publié: (2025)

Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions
par: Ma, Lu, et autres
Publié: (2025)

Seeing My Future: Predicting Situated Interaction Behavior in Virtual Reality
par: Xu, Yuan, et autres
Publié: (2025)

Measuring the Validity of Clustering Validation Datasets
par: Jeon, Hyeon, et autres
Publié: (2025)

High-Dimensional BWDM: A Robust Nonparametric Clustering Validation Index for Large-Scale Data
par: Baragilly, Mohammed, et autres
Publié: (2025)

Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures
par: Wang, Zeya, et autres
Publié: (2024)

Improving Clustering on Occupational Text Data through Dimensionality Reduction
par: García, Iago Xabier Vázquez, et autres
Publié: (2025)

The Why and How of Convex Clustering
par: Chi, Eric C., et autres
Publié: (2025)

Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers
par: Curth, Alicia, et autres
Publié: (2024)

LongRecall: A Structured Approach for Robust Recall Evaluation in Long-Form Text
par: Ardestani, MohamamdJavad, et autres
Publié: (2025)

ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression
par: Liu, Guangda, et autres
Publié: (2024)