Explaining t-SNE embeddings locally by adapting LIME

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Abstract

Non-linear dimensionality reduction techniques, such as tSNE, are widely used to visualize and analyze high-dimensional datasets. While non-linear projections can be of high quality, it is hard, or even impossible, to interpret the dimensions of the obtained embeddings. This paper adapts LIME to locally explain t-SNE embeddings. More precisely, the sampling and black-box-querying steps of LIME are modified so that they can be used to explain t-SNE locally. The result of the proposal is to provide, for a particular instance x and a particular t-SNE embedding Y, an interpretable model that locally explains the projection of x on Y.

Original languageEnglish
Title of host publicationESANN 2020
Subtitle of host publication28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Place of PublicationBruges, Belgium
PublisherESANN (i6doc.com)
Pages393-398
ISBN (Electronic)978-287587074-2
ISBN (Print)9978-2-87587-073-5
Publication statusPublished - 21 Oct 2020
Event28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN2020 - Bruges, Belgium
Duration: 2 Oct 20204 Oct 2020

Conference

Conference28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Country/TerritoryBelgium
CityBruges
Period2/10/204/10/20

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