Approximate nearest neighbor search (ANNS) constitutes an important operation in a
multitude of applications, including recommendation systems, information retrieval, and …

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from
data mining, recommendation system and Internet of Things, to Industry 4.0 framework …

This paper focuses on scalability and robustness of spectral clustering for extremely large-
scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra …

Nearest neighbor search is a fundamental and essential operation in applications from
many domains, such as databases, machine learning, multimedia, and computer vision …

Machine learning can provide deep insights into data, allowing machines to make high-
quality predictions and having been widely used in real-world applications, such as text …

Approximate nearest neighbor (ANN) search is a fundamental problem in many areas of
data mining, machine learning and computer vision. The performance of traditional …

How can we find the right graph for semi-supervised learning? In real world applications, the
choice of which edges to use for computation is the first step in any graph learning process …

The Mutual Information (MI) is an often used measure of dependency between two random
variables utilized in information theory, statistics and machine learning. Recently several MI …

Community detection involves grouping the nodes of a network such that nodes in the same
community are more densely connected to each other than to the rest of the network …

In the literature, most existing graph-based semi-supervised learning methods only use the
label information of observed samples in the label propagation stage, while ignoring such …