High-Dimensional Data Visualization via Sampling-Based Approaches
Measurement of structural similarity between different embeddings as a way of predicting a suitable perplexity
R. Chiriac (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Martin Skrodzki – Mentor (TU Delft - Computer Graphics and Visualisation)
K Hildebrandt – Mentor (TU Delft - Computer Graphics and Visualisation)
C. Lofi – Graduation committee member (TU Delft - Web Information Systems)
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Abstract
Dimensionality reduction techniques, such as t-SNE, are widely used to visualize high-dimensional data and have a crucial role in practical tasks such as biological data exploration, anomaly detection, or clustering large datasets. However, they are highly dependent on hyperparameters or sampling strategies. This paper investigates whether the structural similarity between sampled and full embeddings can be measured using Procrustes analysis by comparing the structural similarity of the embeddings. This work provides a reproducible framework that quantifies the difference between visualizations produced by sampling t-SNE. These insights provide users a medium to create visualizations with t-SNE without exhaustive experimentation (for example, creating all visualizations), making t-SNE more accessible and reliable.