Optimizing graph layout by t-SNE perplexity estimation

Xiao, C; Hong, S; Huang, W

Optimizing graph layout by t-SNE perplexity estimation

Xiao, C

Hong, S Huang, W

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: International Journal of Data Science and Analytics, 2022, pp. 1-13
Issue Date:: 2022-07-30

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Field	Value	Language
dc.contributor.author	Xiao, C https://orcid.org/0000-0002-0725-1107
dc.contributor.author	Hong, S
dc.contributor.author	Huang, W
dc.date.accessioned	2022-08-29T02:02:32Z
dc.date.available	2022-08-29T02:02:32Z
dc.date.issued	2022-07-30
dc.identifier.citation	International Journal of Data Science and Analytics, 2022, pp. 1-13
dc.identifier.issn	2364-415X
dc.identifier.issn	2364-4168
dc.identifier.uri	http://hdl.handle.net/10453/161004
dc.description.abstract	<jats:title>Abstract</jats:title><jats:p>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.</jats:p>
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation	http://purl.org/au-research/grants/arc/LP160100935
dc.relation.ispartof	International Journal of Data Science and Analytics
dc.relation.isbasedon	10.1007/s41060-022-00348-7
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0801 Artificial Intelligence and Image Processing
dc.title	Optimizing graph layout by t-SNE perplexity estimation
dc.type	Journal Article
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Research Office
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Research Office/Research Office (Intelligence)
utslib.copyright.status	open_access	*
dc.date.updated	2022-08-29T02:02:30Z
pubs.publication-status	Published online

Abstract:

AbstractPerplexity 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.

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http://hdl.handle.net/10453/161004