Specifically, we first show the convergence\nof the graph Laplacian regularizer to a continuous-domain functional,\nintegrating a norm measured in a locally adaptive metric space. Focusing on\nimage denoising, we derive an optimal metric space assuming non-local\nself-similarity of pixel patches, ...
Processing1GraphLaplacianRegularizationforImageDenoising:AnalysisintheContinuousDomainJiahaoPang,Member,IEEE,GeneCheung,SeniorMember,IEEEAbstract—Inverseimagingproblemsareinherentlyunder-determined,andhenceitisimportanttoemployappropriateimagepriorsforregularization.Onerecentpopularprior—thegraphLaplacianregularizer—assumesthat...
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 ...
where L and A [26] are the Laplacian and adjacency operators, respectively, and w is the vector of graph weights. While the estimator proposed in Kumar et al. [26] does not directly assume knowledge of the partition of the node set, it does require the availability of the number of nod...
Furthermore, to avoid the effect of the predefined graph quality, [9] introduces a disagreement cost function and constrains the rank of the Laplacian matrix of the learned graph. However, these methods merely focus on optimal weight learning for each view and neglect the local spatial ...
Several corroborating numerical tests using real datasets are provided to showcase the merits of the graph-regularized MCCA variants relative to several competing alternatives including MCCA, Laplacian-regularized MCCA, and (graph-regularized) PCA. ...
Although p-Laplacian metric has tighter isoperimetric inequality for representing the intrinsic structure [1], it is hard work to precisely describe the manifold structure of the entire data space though the single distance metric. The graph model usually is constructed based on the distance metric....
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 ...
Using the Laplacian quadratic form 𝑡𝑟(𝐗𝑇ℒ(𝐖)𝐗)trXTL(W)X as a smoothness regularizer of the data 𝐱𝑛xn, and the degree of connectivity K as a tuning parameter, ref. [11] discovers a K-sparse graph from noisy signals 𝐘Y. This is the result of solving the ...
This paper first train a domain-guided encoder, and then propose domain-regularized optimization by involving the encoder as a regularizer to finetune the code produced by the encoder and better recover the target image. Method: (1) Objective for training encoder: MSE loss and perceptual loss ...