Results 1 to 10 of about 666,730 (159)
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 +3 more
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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 +4 more
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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 +4 more
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Regular colorings in regular graphs
Discussiones Mathematicae Graph Theory, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Anton Bernshteyn +6 more
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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 +4 more
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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 +5 more
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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
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Remote 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
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Degrees in Link Graphs of Regular Graphs
The Electronic Journal of Combinatorics, 2022 We analyse an extremal question on the degrees of the link graphs of a finite regular graph, that is, the subgraphs induced by non-trivial spheres. We show that if $G$ is $d$-regular and connected but not complete then some link graph of $G$ has minimum degree at most $\lfloor{2d/3}\rfloor-1$, and if $G$ is sufficiently large in terms of $d$ then some ...
Benjamini, I, Haslegrave, J
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IEEE Access, 2014 A new regularization technique for graph Laplacians arising from triangular meshes of closed and open structures is presented. The new technique is based on the analysis of graph Laplacian spectrally equivalent operators in terms of Sobolev norms and on ...
Rajendra Mitharwal +1 more
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