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

Ye Zhu; Kai Ming Ting

IJCAI 2022poster9 citations

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

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.

Data Mining: Data VisualisationMachine Learning: Feature Extraction, Selection and Dimensionality Reduction

BibTeX

@inproceedings{ijcai2022p812,
  title     = {Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract)},
  author    = {Zhu, Ye and Ting, Kai Ming},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {5792--5796},
  year      = {2022},
  month     = {7},
  note      = {Journal Track},
  doi       = {10.24963/ijcai.2022/812},
  url       = {https://doi.org/10.24963/ijcai.2022/812},
}