This paper introduces a graph Laplacian regularization in the hyperspectral unmixing formulation. The proposed regularization relies upon the construction of a graph representation of the hyperspectral image. Each node in the graph represents a pixel's spectrum, and edges connect similar pixels. The ...
本文通过使用 Graph Laplacian Regularization 来建模图结构和节点特征。其假设相连的两个节点更有可能属于同一类,从而迫使相邻节点具有相似的节点嵌入。这种简单的启发式假设无法涵盖图中节点的复杂关系,特别是位于决策边界附近的节点,其实际上是预测不可靠的节点。因此引入边可靠度作为正则化的一种方法。
Graph Laplacian Regularized Graph ConvolutionalNetworks for Semi-supervised LearningBo Jiang, Doudou LinSchool of Computer Science and TechnologyAnhui UniversityHefei, Chinajiangbo@ahu.edu.cnAbstractRecently, graph convolutional network (GCN) has been widely used for semi-supervised classif i cation and de...
4.1Equivalence of Squared-Error P-Reg to Squared Laplacian Regularization 4.2 Equivalence of Minimizing P-Reg to Infinite-Depth GCN 5.为什么P-reg可以提升GNN的性能? 5.1从图正则化视角来看P-reg的优点 5.2 Benefits of P-Reg from the Deep GCN Perspective 6.实验 6.1 Improvements on Node Classificatio...
Graph Laplacian Regularization: In machine learning, the Laplacian matrix is commonly used for regularization in graph-based semi-supervised learning, where the goal is to predict labels for nodes in a graph using a combination of labeled and unlabeled data.Example...
(L2), graph Laplacian regularization terms and theL2, 1-norm were added into the traditional NMF model for predicting miRNA-disease connections. In addition, the Tikhonov regularization was utilized to penalize the non-smoothness ofWandH, and the graph Laplacian regularization was primarily intended ...
2. 代码实现(Pytorch):https://github.com/tkipf/pygcn 【Introduction】: 本文尝试用 GCN 进行半监督的分类,通过引入一个 graph Laplacian regularization term 到损失函数中: 其中,L0 代表损失函数,即:graph 的标注部分,f(*) 可以是类似神经网络的可微分函数,X 是节点特征向量组成的矩阵, 代表 无向图 g ...
This amounts to solving a problem with a scaled spectrum of the Laplacian. The regularization constant \(\omega\) in (17) balances the interplay between the data misfit term and the structure-preserving regularization term. If its value is too small, the multi-fidelity model is likely to ...
Full waveform inversion is a nonlinear fitting technique that requires regularization methods to alleviate ill-posedness. Based on the newly introduced Graph-Laplacian regularization, which offers outstanding manifold learning, we propose a novel regularization method for FWI that can combine the graph Lapl...
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