The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

The need for appropriate ways to measure the distance or similarity between data is
ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such …

Self-training methods have gained significant attention in recent years due to their
effectiveness in leveraging small labeled datasets and large unlabeled observations for …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …

Graph neural networks have become one of the most important techniques to solve machine
learning problems on graph-structured data. Recent work on vertex classification proposed …

In this paper, we design a benchmark task and provide the associated datasets for
recognizing face images and link them to corresponding entity keys in a knowledge base …

Abstract Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a
handful of labeled data to the remaining massive unlabeled data via a graph. As one of the …

We propose a sampling theory for signals that are supported on either directed or undirected
graphs. The theory follows the same paradigm as classical sampling theory. We show that …

Graph-based methods have been demonstrated as one of the most effective approaches for
semi-supervised learning, as they can exploit the connectivity patterns between labeled and …

In recent years, a variety of nonlinear dimensionality reduction techniques have been
proposed that aim to address the limitations of traditional techniques such as PCA. The …