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Optimizing graph layout by t-SNE perplexity estimation
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DOI：10.1007/s41060-022-00348-7.png)
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Abstract
En 中文
Perplexity is one of the key parameters of dimensionality reduction algorithm of t-distributed stochastic neighbor embedding (t-SNE). In this paper, we investigated the relationship of t-SNE perplexity and graph layout evaluation metrics including graph stress, preserved neighborhood information and visual inspection. As we found that a small perplexity is correlated with a relative higher normalized stress while preserving neighborhood information with a higher precision but less global structure information, we proposed our method to estimate appropriate perplexity either based on a modified standard t-SNE or the sklearn Barnes-Hut TSNE. Experimental results demonstrate effectiveness and ease of use of our approach when tested on a set of benchmark datasets.
Keywords:
Data visualization
Dimensionality reduction
Graph layout
Graph
network data
Perplexity
t-SNE
Journal
I
IF:
2.8
Papers:
1.0K
Citations:
1.3K