Abstract: Several applications in different engineering areas require the computation of the Euclidean distance, a quite complex operation based on squaring and ...

Machine learning has expanded beyond traditional Euclidean spaces in recent years, exploring representations in more complex geometric structures. Non-Euclidean representation learning is a growing ...

Distance calculation is essential in various fields like physics, mathematics, engineering, geography, and everyday life. Knowing how to measure distance accurately and efficiently allows us to ...

This repository contains a Python implementation of a K-Nearest Neighbors (KNN) classifier from scratch. It's applied to the "BankNote_Authentication" dataset, which consists of four features ...

Dr. James McCaffrey of Microsoft Research shows how to compute the Wasserstein distance and explains why it is often preferable to alternative distance functions, used to measure the distance between ...

I am following the Python code example on the front page on iPython notebook, but if I change metric to Euclidean python crashes when I run u.get_nns_by_item. Has anyone else encountered this problem?