Graph regularization-based methods have achieved great success for network classification by making the label-link consistency assumption, i.e., if two nodes are linked together, they are likely to belong to the same class. However, in a real-world network, there exist links that connect nodes of different classes. These inconsistent links raise a big challenge for graph regularization and dete...

Graph-embedding learning is the foundation of complex information network analysis, aiming to represent nodes in a graph as low-dimensional dense real-valued vectors for application practical analysis tasks. In recent years, study representation has received increasing attention from researchers, and, among them, neural networks (GNNs) based on deep are playing an increasingly important role th...

We consider the sparse inverse covariance regularization problem or graphical lasso with regularization parameter λ. Suppose the sample covariance graph formed by thresholding the entries of the sample covariance matrix at λ is decomposed into connected components. We show that the vertex-partition induced by the connected components of the thresholded sample covariance graph (at λ) is exactly ...

in this paper main aim is to focus on to remove impulse noise from corrupted image. Here present a method for removing noise from digital images corrupted with additive, multiplicative, and mixed noise. Here used hybrid graph Laplacian regularized regression to perform progressive image recovery using unified framework. by using laplacian pyramid here build multi-scale representation of input i...

The incorporation of region regularization into max-flow segmentation has traditionally focused on ordering and part-whole relationships. A side effect of the development of such models is that it constrained regularization only to those cases, rather than allowing for arbitrary region regularization. Directed Acyclic Graphical MaxFlow (DAGMF) segmentation overcomes these limitations by allowin...

In semi-supervised learning (SSL), we learn a predictive model from a collection of labeled data and a typically much larger collection of unlabeled data. These lecture notes present a framework called multi-view point cloud regularization (MVPCR) [5], which unifies and generalizes several semi-supervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbe...

In semi-supervised learning (SSL), we learn a predictive model from a collection of labeled data and a typically much larger collection of unlabeled data. These lecture notes present a framework called multi-view point cloud regularization (MVPCR) [5], which unifies and generalizes several semi-supervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbe...

In this paper, we study the manifold regularization for the Sliced Inverse Regression (SIR). The manifold regularization improves the standard SIR in two aspects: 1) it encodes the local geometry for SIR and 2) it enables SIR to deal with transductive and semi-supervised learning problems. We prove that the proposed graph Laplacian based regularization is convergent at rate root-n. The projecti...

The methods applied to regularization of the ill-posed problems can be classified under “direct” and “indirect” methods. Practice has shown that the effects of different regularization techniques on an ill-posed problem are not the same, and as such each ill-posed problem requires its own investigation in order to identify its most suitable regularization method. In the geoid computations witho...

We propose in this work to regularize the output of a svm classifier on pixels in order to promote smoothness in the predicted image. The learning problem can be cast as a semi-supervised SVM with a particular structure encoding pixel neighborhood in the regularization graph. We provide several optimization schemes in order to solve the problem for linear SVM with `2 or `1 regularization and sh...