Parametric Dimension Reduction by Preserving Local Structure

Chien Hsun Lai*, Ming Feng Kuo, Yun Hsuan Lien, Kuan An Su, Yu Shuen Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


We extend a well-known dimension reduction method, t-distributed stochastic neighbor embedding (t-SNE), from non-parametric to parametric by training neural networks. The main advantage of a parametric technique is the generalization of handling new data, which is beneficial for streaming data visualization. While previous parametric methods either require a network pre-training by the restricted Boltzmann machine or intermediate results obtained from the traditional non-parametric t-SNE, we found that recent network training skills can enable a direct optimization for the t-SNE objective function. Accordingly, our method achieves high embedding quality while enjoying generalization. Due to mini-batch network training, our parametric dimension reduction method is highly efficient. For evaluation, we compared our method to several baselines on a variety of datasets. Experiment results demonstrate the feasibility of our method. The source code is available at

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE Visualization Conference - Short Papers, VIS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781665488129
StatePublished - 2022
Event2022 IEEE Visualization Conference, VIS 2022 - Virtual, Online, United States
Duration: 16 Oct 202221 Oct 2022

Publication series

NameProceedings - 2022 IEEE Visualization Conference - Short Papers, VIS 2022


Conference2022 IEEE Visualization Conference, VIS 2022
Country/TerritoryUnited States
CityVirtual, Online


  • Computing methodologies
  • Dimensionality reduction and manifold learning
  • Human-centered computing
  • Visualization toolkits


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