Results 1 to 10 of about 24,126 (306)

Node-Adaptive Regularization for Graph Signal Reconstruction

IEEE Open Journal of Signal Processing, 2021
A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem.
Maosheng Yang, Mario Coutino, Geert Leus, Elvin Isufi   +3 more
doaj   +2 more sources

Neural Networks Regularization With Graph-Based Local Resampling

IEEE Access, 2021
This paper presents the concept of Graph-based Local Resampling of perceptron-like neural networks with random projections (RN-ELM) which aims at regularization of the yielded model.
Alex D. Assis, Luiz C. B. Torres, Lourenco R. G. Araujo, Vitor M. Hanriot, Antonio P. Braga   +4 more
doaj   +2 more sources

Multi relational dual attention graph transformer for fine grained sentiment analysis [PDF]

Scientific Reports
Aspect-Based Sentiment Analysis requires precise identification of sentiment polarity toward specific aspects, demanding robust modeling of syntactic, semantic, and discourse-level dependencies.
Anusha P. Anilkumar, Soo-Kyun Kim, Yeo-Chan Yoon   +2 more
doaj   +2 more sources

GCOA-Net: a graph-regularized cross-omics attention network for interpretable breast cancer molecular subtype classification [PDF]

Frontiers in Medicine
IntroductionAccurate intrinsic molecular subtyping is essential for precision management of breast cancer, yet multi-omics integration remains challenging due to high dimensionality, structured cross-omics dependencies, and the need for clinically ...
Chen Li, Zhen Zhang, Chun Zhang
doaj   +2 more sources

Graph-Enhanced Expectation Maximization for Emission Tomography [PDF]

Journal of Imaging
Emission tomography, including single-photon emission computed tomography (SPECT), requires image reconstruction from noisy and incomplete projection data.
Ryosuke Kasai, Hideki Otsuka
doaj   +2 more sources

Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark

Frontiers in Artificial Intelligence, 2022
In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a ...
Antonio Carta, Andrea Cossu, Andrea Cossu, Federico Errica, Davide Bacciu   +4 more
doaj   +1 more source

Regular colorings in regular graphs

Discussiones Mathematicae Graph Theory, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Anton Bernshteyn, Omid Khormali, Ryan R. Martin, Jonathan Rollin, Danny Rorabaugh, Songling Shan, Andrew J. Uzzell   +6 more
openaire   +3 more sources

Hyperspectral Image Super-Resolution Algorithm Based on Graph Regular Tensor Ring Decomposition

Remote Sensing, 2023
This paper introduces a novel hyperspectral image super-resolution algorithm based on graph-regularized tensor ring decomposition aimed at resolving the challenges of hyperspectral image super-resolution.
Shasha Sun, Wenxing Bao, Kewen Qu, Wei Feng, Xiaowu Zhang, Xuan Ma   +5 more
doaj   +1 more source

Semisupervised Hyperspectral Image Classification via Superpixel-Based Graph Regularization With Local and Nonlocal Features

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Although a graph-based semisupervised learning (SSL) approach can utilize limited numbers of labeled samples for hyperspectral image (HSI) classification, it is difficult to use the large amount of pixels in an HSI to construct a large-scale graph.
Longshan Yang, Junhuan Peng, Yuebin Wang, Linlin Xu, Weiwei Zhu   +4 more
doaj   +1 more source

PolSAR Image Feature Extraction via Co-Regularized Graph Embedding

Remote Sensing, 2020
Dimensionality reduction (DR) methods based on graph embedding are widely used for feature extraction. For these methods, the weighted graph plays a vital role in the process of DR because it can characterize the data’s structure information.
Xiayuan Huang, Xiangli Nie, Hong Qiao
doaj   +1 more source