T-SNE Is Not Optimized to Reveal Clusters in Data

• URL: http://arxiv.org/abs/2110.02573v1
• Date: Wed, 6 Oct 2021 08:35:39 GMT
• Title: T-SNE Is Not Optimized to Reveal Clusters in Data
• Authors: Zhirong Yang, Yuwei Chen, Jukka Corander
• Abstract summary: Cluster visualization is an essential task for nonlinear dimensionality reduction as a data analysis tool. It is often believed that Student t-Distributed Neighbor Embedding (t-SNE) can show clusters for well clusterable data. We show that t-SNE may leave clustering patterns hidden despite strong signals present in the data.
• Score: 4.03823460330412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cluster visualization is an essential task for nonlinear dimensionality reduction as a data analysis tool. It is often believed that Student t-Distributed Stochastic Neighbor Embedding (t-SNE) can show clusters for well clusterable data, with a smaller Kullback-Leibler divergence corresponding to a better quality. There was even theoretical proof for the guarantee of this property. However, we point out that this is not necessarily the case -- t-SNE may leave clustering patterns hidden despite strong signals present in the data. Extensive empirical evidence is provided to support our claim. First, several real-world counter-examples are presented, where t-SNE fails even if the input neighborhoods are well clusterable. Tuning hyperparameters in t-SNE or using better optimization algorithms does not help solve this issue because a better t-SNE learning objective can correspond to a worse cluster embedding. Second, we check the assumptions in the clustering guarantee of t-SNE and find they are often violated for real-world data sets.

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