AI RESEARCH
On Linear Separability of the MNIST Handwritten Digits Dataset
arXiv CS.LG
•
ArXi:2603.12850v1 Announce Type: new The MNIST dataset containing thousands of handwritten digit images is still a fundamental benchmark for evaluating various pattern-recognition and image-classification models. Linear separability is a key concept in many statistical and machine-learning techniques. Despite the long history of the MNIST dataset and its relative simplicity in size and resolution, the question of whether the dataset is linearly separable has never been fully answered -- scientific and informal sources share conflicting claims.