Other approaches rely on using a Graph Laplacian regularizer. Here we propose a combination of these two approaches that can be applied to L1, L2 and LS-SVMs. We also propose an algorithm to iteratively learn the graph adjacency matrix used in the Laplacian regularization. We test our proposal...
主题 这篇文章的主题是基于梯度图拉普拉斯正则化(Gradient Graph Laplacian Regularizer, GGLR)的图信号恢复方法,尤其是在流形图(manifold graphs)上的应用。这种方法旨在恢复具有渐变或分段渐变特性的信号(例如图像或点云的重建),克服了传统方法(如GLR)在处理渐变信号时出现的“阶梯效应”(staircase effect)。 核心创新...
There are four contributions in our work: (1) We show that the sparse graph Laplacian regularizer as an effective prior is applicable to excavate the nonlocal similarity of the model. (2) By applying the sparsity and non-local similarity in the inversion simultaneously, we derived more ...
We observe the distances between estimated function outputs on data points to create an anisotropic graph Laplacian which, through an iterative process, can itself be regularized. Our algorithm is instantiated as a discrete regularizer on a graph’s diffusivity operator. This idea is grounded in the...
Different from the existing low-rank subspace learning methods, LGIRPCA considers the feature manifold structure of a given data set and designs a new Laplacian regularizer to characterize the structure information. We proved that the devised Laplacian regularizer could be transferred to be a weighted...
The proposed framework forms the Laplacian regularizer through learning the affinity graph. We incorporate the new Laplacian regularizer into the unsupervised data representation to smooth the low dimensional representation of data and make use of label information. Experimental results on several real ...
model, namely LSTM-GL-ReMF, as the key component of the framework. The Long Short-term Memory (LSTM) model is chosen as the temporal regularizer to capture temporal dependency intime series dataand the Graph Laplacian (GL) serves as the spatial regularizer to utilizespatial correlationsamong ...
In particular, each sample is represented by the whole dataset regularized with _2 -norm and Laplacian regularizer. Then a MRLSR graph is constructed based on the representative coefficients of each sample. Moreover, we present two optimization schemes to generate refined-graphs by employing a ...
The graph-based semi-supervised technique makes the input data built on the full graph, which combines the labeled and unlabeled nodes by employing a graph Laplacian regularizer when training and evaluating node classification models. The unlabeled nodes are completely observed during training or ...
To accomplish this, a Laplacian graph matrix is typically defined to encode the graph’s topology, which is used in the convolution operation to aggregate information from neighboring nodes. The fundamental concept behind GCNs is to perform convolutions on graphs like how convolutions are performed ...