K-nearest neighbor graphs (K-NNGs) are used in many data-mining and machine-learning
algorithms. Naive construction of K-NNGs has a complexity of O (n2), which could be a …

Due to the high complexity of constructing exact k-nearest neighbor graphs, approximate
construction has become a popular research topic. The NN-Descent algorithm is one of the …

Clustering items using textual features is an important problem with many applications, such
as root-cause analysis of spam campaigns, as well as identifying common topics in social …

The application of the diffusion in many computer vision and artificial intelligence projects
has been shown to give excellent improvements in performance. One of the main …

The K-nearest neighbor graph (K-NNG) is a data structure used by many machine-learning
algorithms. Naive computation of the K-NNG has quadratic time complexity, which in many …

Experimental results demonstrated the goodness of the diffusion mechanism for several
computer vision tasks: image retrieval, semi-supervised and supervised learning, image …

Abstract K-Nearest Neighbors (KNN) is a crucial tool for many applications, eg
recommender systems, image classification and web-related applications. However, KNN is …

K-nearest neighbors (KNN) is a crucial tool for many applications, eg recommender systems,
image classification and web-related applications. However, KNN is a resource greedy …

Extended Curriculum Vitae Page 1 Extended Curriculum Vitae Prof. Pietro Michiardi, Ph.D.
Appointments 2019 – now Full Professor, EURECOM, France 2016 – now Director of the Data …

Nearest neighbor graphs are modeling proximity relationships between objects. They are
widely used in many areas, primarily in machine learning, but also in information retrieval …