Work

Explaining t-SNE Embeddings Locally by Adapting LIME.

Year: 2020

Type: conference-paper

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.

Source: Repository of the University of Namur

Authors: Adrien Bibal, Viet Minh Vu, Géraldin Nanfack, Benoît Frénay‬

Institutions: Namur Digital Institute, Institut de Recherches en Didactiques et Éducation de l’UNamur

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Fonds De La Recherche Scientifique - FNRS

Award: G2156223344

OpenAlex ID: W3048700964