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17 papers match

cs.LG2026

Measuring Distortion in the Empty Regions of Dimensionality Reduction Scatterplots with the Gap Index

Jaume Ros, Alessio Arleo, Fernando Paulovich

The paper proposes the Gap Index, a metric that measures how much empty regions in 2D dimensionality‑reduction scatterplots are distorted compared to the original high‑dimensional…

#dimensionality reduction#visualization#quality metrics#spatial distortion
eess.SY2026

Some intuition for why cooperative systems "look 1-dimensional" and 2-cooperative systems "look 2-dimensional"

Eduardo D. Sontag

The paper offers geometric intuition for why cooperative (monotone) systems behave like one-dimensional systems and two‑cooperative systems behave like two-dimensional systems, usi…

#cooperative systems#monotone systems#dimensionality reduction#projective metric
cs.LG2026

Voronoi Histograms for Adaptive Vectorization of Expected Persistence Diagrams

Kaifeng Zhang, Kai Ming Ting

The paper introduces a Voronoi histogram method to vectorize expected persistence diagrams, providing an adaptive, partition-based representation that retains topological informati…

#topological data analysis#persistence diagrams#vectorization#voronoi histograms
cs.LG2026

FADEx: Feature Attribution and Distortion-based Explanation of Dimensionality Reduction

Lucas Greff Meneses, Evandro S. Ortigossa, Claudio Silva +1

The paper introduces FADEx, a local per-instance feature attribution method that explains how individual features influence the placement of data points in any dimensionality reduc…

#dimensionality reduction#feature attribution#explainable ai#local linear approximation
cs.LG2026

Orthogonality and Dimensionality in Airline Cluster Analysis using PCA and Kernel PCA

Andreas Schlapbach

The paper replicates a US airline profit-cycle clustering study, showing that k‑means clustering on principal component scores yields the same six‑cluster solution as the raw data,…

#cluster analysis#principal component analysis#kernel pca#dimensionality reduction
stat.ML2026

cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data

Chun-houh Chen, Shun-Chuan Chang, Chiun-How Kao +5

The paper presents cGAP, a visualization framework that uses HOMALS embeddings and heatmaps to display high‑dimensional categorical data while preserving the original data matrix a…

#categorical data visualization#homals embedding#heatmaps#dimensionality reduction