English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a way to use Topological Data Analysis (TDA) for machine learningtasks on grayscale images. We apply persistent homology to generate a widerange of topological features using a point cloud obtained from an image, itsnatural grayscale filtration, and different filtrations defined on thebinarized image. We show that this topological machine learning pipeline can beused as a highly relevant dimensionality reduction by applying it to the MNISTdigits dataset. We conduct a feature selection and study their correlationswhile providing an intuitive interpretation of their importance, which isrelevant in both machine learning and TDA. Finally, we show that we canclassify digit images while reducing the size of the feature set by a factor 5compared to the grayscale pixel value features and maintain similar accuracy.

A Topological "Reading" Lesson: Classification of MNIST using TDA