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12 April 2021 t-SNE or not t-SNE, that is the question
Donald Waagen, Donald Hulsey, Jamie Godwin, David Gray, Jonathan Barton, Brett Farmer
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Proceedings Volume 11729, Automatic Target Recognition XXXI; 117290B (2021) https://doi.org/10.1117/12.2585535
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
Abstract
T-distributed Stochastic Neighbor Embedding (t-SNE) has become an extremely popular algorithm for low- dimensional visualization of high dimensional data. While it is acknowledged that it is highly sensitive to its parameters, it continues to be used extensively by the machine learning community, with `intuition' an accepted basis for embedding selection. In this paper, we will illustrate and explain why t-SNE is not a distance preserving algorithm, but rather order preserving, with the cardinality of the order proportional to the perplexity parameter. We compare and contrast t-SNE with Sammon Nonlinear Mappings locally using Kruskal Stress and Spearman Rank Correlation measures.
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Donald Waagen, Donald Hulsey, Jamie Godwin, David Gray, Jonathan Barton, and Brett Farmer "t-SNE or not t-SNE, that is the question", Proc. SPIE 11729, Automatic Target Recognition XXXI, 117290B (12 April 2021); https://doi.org/10.1117/12.2585535
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KEYWORDS
Machine learning
Stochastic processes
Visualization
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