Semi-supervised learning (SSL) has tremendous value in practice due to the utilization of
both labeled and unlabelled data. An essential class of SSL methods, referred to as graph …

Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Semi-supervised learning is the branch of machine learning concerned with using labelled
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …

We propose a framework to learn the graph structure underlying a set of smooth signals.
Given X∈\mathbbR^ m\times n whose rows reside on the vertices of an unknown graph, we …

Different aspects of the curse of dimensionality are known to present serious challenges to
various machine-learning methods and tasks. This paper explores a new aspect of the …

Conventional appearance-based face recognition methods usually assume that there are
multiple samples per person (MSPP) available for discriminative feature extraction during …

Constructing a good graph to represent data structures is critical for many important machine
learning tasks such as clustering and classification. This paper proposes a novel non …

Graph-based semi-supervised learning (GSSL) serves as a powerful tool to model the
underlying manifold structures of samples in high-dimensional spaces. It involves two …

Open-world semi-supervised learning aims at inferring both known and novel classes in
unlabeled data, by harnessing prior knowledge from a labeled set with known classes …