Graph neural networks: foundation, frontiers and applications
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 …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Iterative deep graph learning for graph neural networks: Better and robust node embeddings
In this paper, we propose an end-to-end graph learning framework, namely\textbf {I}
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …
Computational intelligence in gait research: a perspective on current applications and future challenges
Our mobility is an important daily requirement so much so that any disruption to it severely
degrades our perceived quality of life. Studies in gait and human movement sciences …
degrades our perceived quality of life. Studies in gait and human movement sciences …
Discriminative deep metric learning for face verification in the wild
This paper presents a new discriminative deep metric learning (DDML) method for face
verification in the wild. Different from existing metric learning-based face verification …
verification in the wild. Different from existing metric learning-based face verification …
Discriminative deep metric learning for face and kinship verification
This paper presents a new discriminative deep metric learning (DDML) method for face and
kinship verification in wild conditions. While metric learning has achieved reasonably good …
kinship verification in wild conditions. While metric learning has achieved reasonably good …
Deep transfer metric learning
Conventional metric learning methods usually assume that the training and test samples are
captured in similar scenarios so that their distributions are assumed to be the same. This …
captured in similar scenarios so that their distributions are assumed to be the same. This …
Semi-supervised constrained clustering: An in-depth overview, ranked taxonomy and future research directions
G González-Almagro, D Peralta, E De Poorter… - ar** discrete sets of instances with similar characteristics. Constrained …
Semi-supervised clustering with metric learning: An adaptive kernel method
Most existing representative works in semi-supervised clustering do not sufficiently solve the
violation problem of pairwise constraints. On the other hand, traditional kernel methods for …
violation problem of pairwise constraints. On the other hand, traditional kernel methods for …
Kernel-Based Distance Metric Learning for Supervised -Means Clustering
B Nguyen, B De Baets - IEEE transactions on neural networks …, 2019 - ieeexplore.ieee.org
Finding an appropriate distance metric that accurately reflects the (dis) similarity between
examples is a key to the success of k-means clustering. While it is not always an easy task to …
examples is a key to the success of k-means clustering. While it is not always an easy task to …
Online multiple kernel similarity learning for visual search
Recent years have witnessed a number of studies on distance metric learning to improve
visual similarity search in content-based image retrieval (CBIR). Despite their successes …
visual similarity search in content-based image retrieval (CBIR). Despite their successes …