Classes are Not Clusters: Improving Label-Based Evaluation of Dimensionality Reduction

A common way to evaluate the reliability of dimensionality reduction (DR) embeddings is to quantify how well labeled classes form compact, mutually separated clusters in the embeddings. This approach is based on the assumption that the classes stay as clear clusters in the original high-dimensional...

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Veröffentlicht in:	IEEE transactions on visualization and computer graphics Jg. 30; H. 1; S. 781 - 791
Hauptverfasser:	Jeon, Hyeon, Kuo, Yun-Hsin, Aupetit, Michael, Ma, Kwan-Liu, Seo, Jinwook
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.01.2024
Schlagworte:	Clustering Clustering Validation Measures Degradation Dimensionality reduction Dimensionality Reduction Evaluation Distortion measurement Extraterrestrial measurements Nonlinear distortion Reliability Scalability
ISSN:	1077-2626, 1941-0506, 1941-0506
Online-Zugang:	Volltext
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