Toward computing attributions for dimensionality reduction techniques.

ABSTRACT:

Summary

We describe the problem of computing local feature attributions for dimensionality reduction methods. We use one such method that is well established within the context of supervised classification-using the gradients of target outputs with respect to the inputs-on the popular dimensionality reduction technique t-SNE, widely used in analyses of biological data. We provide an efficient implementation for the gradient computation for this dimensionality reduction technique. We show that our explanations identify significant features using novel validation methodology; using synthetic datasets and the popular MNIST benchmark dataset. We then demonstrate the practical utility of our algorithm by showing that it can produce explanations that agree with domain knowledge on a SARS

SUBMITTER: Scicluna M 

PROVIDER: S-EPMC10502234 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature