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

TL;DR

This paper demonstrates how Topological Data Analysis (TDA) can effectively classify MNIST digits by extracting topological features, reducing feature dimensionality, and maintaining high accuracy.

Contribution

It introduces a novel TDA-based pipeline for image classification that reduces feature size while preserving accuracy, with interpretability of topological features.

Findings

Achieved 5-fold feature reduction with maintained accuracy

Identified topological features with strong correlation to digit classes

Provided an intuitive interpretation of topological features in image classification

Abstract

We present a way to use Topological Data Analysis (TDA) for machine learning tasks on grayscale images. We apply persistent homology to generate a wide range of topological features using a point cloud obtained from an image, its natural grayscale filtration, and different filtrations defined on the binarized image. We show that this topological machine learning pipeline can be used as a highly relevant dimensionality reduction by applying it to the MNIST digits dataset. We conduct a feature selection and study their correlations while providing an intuitive interpretation of their importance, which is relevant in both machine learning and TDA. Finally, we show that we can classify digit images while reducing the size of the feature set by a factor 5 compared to the grayscale pixel value features and maintain similar accuracy.