Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning through the lens dual-graph regularization, which has significantly improved performance multidisciplinary machine tasks such as recommendation systems, genotype imputation and image inpainting. While regularization contributes major part success, computational costly hyper-parameter tunning usual...

By exploiting information that is contained in the spatial arrangement of neural activations, multivariate pattern analysis (MVPA) can detect distributed brain activations which are not accessible by standard univariate analysis. Recent methodological advances in MVPA regularization techniques have made it feasible to produce sparse discriminative whole-brain maps with highly specific patterns....

Probabilistic graphical models can simulate and classify high dimensional, heterogeneous data and serve as underlying formalism in the data analysis of biology, physics, computer vision, natural language processing and others. Their parameter dimension is a function of the treewidth of the data's conditional independence graph and the data domain. Even if most dependencies are ignored and a pai...

Super-resolution (SR) aims to overcome the ill-posed conditions of image acquisition. SR facilitates scene recognition from low-resolution image(s). Generally assumes that high and low resolution images share similar intrinsic geometries. Various approaches have tried to aggregate the informative details of multiple low-resolution images into a high-resolution one. In this paper, we present a n...

Spectral clustering consists of two distinct stages: (a) construct an affinity graph from the dataset and (b) cluster the data points through finding an optimal partition of the affinity graph. The focus of the paper is the first step. Existing spectral clustering algorithms adopt Gaussian function to define the affinity graph since it is easy to implement. However, Gaussian function is hard to...

We propose kernel regression for signals over graphs. The optimal regression coefficients are learnt using a constraint that the target vector is a smooth signal over an underlying graph. The constraint is imposed using a graph-Laplacian based regularization. We discuss how the proposed kernel regression exhibits a smoothing effect, simultaneously achieving noise-reduction and graph-smoothness....

In this survey, we go over a few historical literatures on semi-supervised learning problems which apply graph regularization on both labled and unlabeled data to improve classification performance. These semi-supervised methods usually construct a nearest neighbour graph on instance space under certain measure function, and then work under the smoothness assumption that class labels of samples...

A graph-based classification method is proposed both for semi-supervised learning in the case of Euclidean data and for classification in the case of graph data. Our manifold learning technique is based on a convex optimization problem involving a convex regularization term and a concave loss function with a trade-off parameter carefully chosen so that the objective function remains convex. As ...