A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations

Daniel Atzberger*, Tim Cech, Willy Scheibel, Jurgen Dollner, Michael Behrisch, Tobias Schreck

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikelBegutachtung

Abstract

The semantic similarity between documents of a text corpus can be visualized using map-like metaphors based on twodimensional scatterplot layouts. These layouts result from a dimensionality reduction on the document-term matrix or a representation within a latent embedding, including topic models. Thereby, the resulting layout depends on the input data and hyperparameters of the dimensionality reduction and is therefore affected by changes in them. Furthermore, the resulting layout is affected by changes in the input data and hyperparameters of the dimensionality reduction. However, such changes to the layout require additional cognitive efforts from the user. In this work, we present a sensitivity study that analyzes the stability of these layouts concerning (1) changes in the text corpora, (2) changes in the hyperparameter, and (3) randomness in the initialization. Our approach has two stages: data measurement and data analysis. First, we derived layouts for the combination of three text corpora and six text embeddings and a grid-search-inspired hyperparameter selection of the dimensionality reductions. Afterward, we quantified the similarity of the layouts through ten metrics, concerning local and global structures and class separation. Second, we analyzed the resulting 42 817 tabular data points in a descriptive statistical analysis. From this, we derived guidelines for informed decisions on the layout algorithm and highlight specific hyperparameter settings. We provide our implementation as a Git repository at hpicgs/Topic-Models-and-DimensionalityReduction-Sensitivity-Study and results as Zenodo archive at DOI:10.5281/zenodo.12772898.

Originalspracheenglisch
Seiten (von - bis) 305 - 315
FachzeitschriftIEEE Transactions on Visualization and Computer Graphics
Jahrgang31
Ausgabenummer1
DOIs
PublikationsstatusVeröffentlicht - Jan. 2025

ASJC Scopus subject areas

  • Software
  • Signalverarbeitung
  • Maschinelles Sehen und Mustererkennung
  • Computergrafik und computergestütztes Design

Fingerprint

Untersuchen Sie die Forschungsthemen von „A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren