Distance geometry and data science
L Liberti - Top, 2020 - Springer
Data are often represented as graphs. Many common tasks in data science are based on
distances between entities. While some data science methodologies natively take graphs as …
distances between entities. While some data science methodologies natively take graphs as …
How to Measure the Researcher Impact with the Aid of its Impactable Area: A Concrete Approach Using Distance Geometry
Assuming that the subject of each scientific publication can be identified by one or more
classification entities, we address the problem of determining a similarity function (distance) …
classification entities, we address the problem of determining a similarity function (distance) …
Denoising and Completion of Euclidean Distance Matrix from Multiple Observations
SS Natva, S Nannuru - 2024 National Conference on …, 2024 - ieeexplore.ieee.org
For a point set, Euclidean distance matrix (EDM) is the matrix consisting of squared
distances between every pair of points in the set. It frequently appears in wide ranging …
distances between every pair of points in the set. It frequently appears in wide ranging …
Ultra-Wideband Error Modeling for Improved Localization
E Pedlow - 2021 - dspace.mit.edu
Ultra-wideband (UWB) is a modern range measurement technology which can provide high-
speed, low-cost ranging, however UWB measurements can be difficult to model. In an effort …
speed, low-cost ranging, however UWB measurements can be difficult to model. In an effort …