Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract)

IJCAI 2022 pp. 5792-5796

doi:10.24963/IJCAI.2022/812 /ijcai/2022/zhu2022ijcai-improving/

Abstract

This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel. We show that Isolation kernel addresses two deficiencies of t-SNE that employs Gaussian kernel, and the use of Isolation kernel enables t-SNE to deal with large-scale datasets in less runtime without trading off accuracy, unlike existing methods used in speeding up t-SNE.

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Cite

Text

Zhu and Ting. "Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/812

Markdown

[Zhu and Ting. "Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/zhu2022ijcai-improving/) doi:10.24963/IJCAI.2022/812

BibTeX

@inproceedings{zhu2022ijcai-improving,
  title     = {{Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract)}},
  author    = {Zhu, Ye and Ting, Kai Ming},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {5792-5796},
  doi       = {10.24963/IJCAI.2022/812},
  url       = {https://mlanthology.org/ijcai/2022/zhu2022ijcai-improving/}
}