Explaining t-SNE embeddings locally by adapting LIME

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

7 Downloads (Pure)

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 publicationEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Place of PublicationBruges, Belgium
Pages393-398
Publication statusPublished - 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
CountryBelgium
CityBruges
Period2/10/204/10/20

Fingerprint Dive into the research topics of 'Explaining t-SNE embeddings locally by adapting LIME'. Together they form a unique fingerprint.

Cite this