Results 1 to 10 of about 115,562 (111)

Holomorphic subgraph reduction of higher-point modular graph forms

open access: yesJournal of High Energy Physics, 2019
Modular graph forms are a class of modular covariant functions which appear in the genus-one contribution to the low-energy expansion of closed string scattering amplitudes.
Jan E. Gerken, Justin Kaidi
doaj   +3 more sources

GrSrNMF: dynamic community detection with graph and symmetry bi-regularized non-negative matrix factorization [PDF]

open access: yesScientific Reports
Community detection in dynamic networks has become an interesting and popular research direction in recent years, widely used in electronic commerce, social media, and other fields.
Wei Yu   +6 more
doaj   +2 more sources

Graph-Enhanced Expectation Maximization for Emission Tomography [PDF]

open access: yesJournal 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

Node-Adaptive Regularization for Graph Signal Reconstruction

open access: yesIEEE 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   +3 more
doaj   +1 more source

Neural Networks Regularization With Graph-Based Local Resampling

open access: yesIEEE 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   +4 more
doaj   +1 more source

Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark

open access: yesFrontiers 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   +4 more
doaj   +1 more source

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

open access: yesRemote 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   +5 more
doaj   +1 more source

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

open access: yesIEEE 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   +4 more
doaj   +1 more source

PolSAR Image Feature Extraction via Co-Regularized Graph Embedding

open access: yesRemote 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

Improving K-Nearest Neighbor Approaches for Density-Based Pixel Clustering in Hyperspectral Remote Sensing Images

open access: yesRemote Sensing, 2020
We investigated nearest-neighbor density-based clustering for hyperspectral image analysis. Four existing techniques were considered that rely on a K-nearest neighbor (KNN) graph to estimate local density and to propagate labels through algorithm ...
Claude Cariou   +2 more
doaj   +1 more source

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