Symmetric Positive Definite (SPD) matrices have received wide attention in machine learning due to their intrinsic capacity to encode underlying structural correlation in data. Many successful ...

We have come across a paper that proposes a novel distance metric called Dimension Insensitive Euclidean Metric (DIEM), which improves on cosine similarity for multidimensional comparisons, ...

A distance metric is a function that quantifies how far apart two data points are. It can be based on different criteria, such as the Euclidean distance (the straight-line distance), the Manhattan ...

For portability, it’s nice to have a folding set of hex wrenches. You can’t lose an individual wrench, and it’s great for tossing into a tool bag. Our favorites are the Bondhus 12522 ...

In many classification scenarios, the data to be analyzed can be naturally represented as points living on the curved Riemannian manifold of symmetric positive-definite (SPD) matrices. Due to its ...

Deep metric learning aims to train a similarity metric that uses samples to compute the similarity or dissimilarity of two or more objects. Face recognition, face verification, person ...

In the early 20th century, Albert Einstein used Riemann’s metric tensor to develop General Relativity: a four-dimensional theory of spacetime and gravitation.