To investigate the effect of coupling edges, we compare cooperative cut (CoopCut) to the standard graph cut (GraphCut), and, for shrinking bias, also to curvature regularization. To ensure equivalent conditions, all methods used the same weights on the terminal edges (i.e., the same unary potentials), the same 8-neighbor graph structure, and the same inter-pixel edge weights. The unary potentia...

We present a simple, yet effective, approach to Semi-Supervised Learning. Our approach is based on estimating density-based distances (DBD) using a shortest path calculation on a graph. These Graph-DBD estimates can then be used in any distancebased supervised learning method, such as Nearest Neighbor methods and SVMs with RBF kernels. In order to apply the method to very large data sets, we al...

We propose an algorithmic framework for convex minimization problems of composite functions with two terms: a self-concordant part and a possibly nonsmooth regularization part. Our method is a new proximal Newton algorithm with local quadratic convergence rate. As a specific problem instance, we consider sparse precision matrix estimation problems in graph learning. Via a careful dual formulati...

in this paper the 3d inversion of gravity data using two different regularization methods, namely tikhonov regularization and truncated singular value decomposition (tsvd), is considered. the earth under the survey area is modeled using a large number of rectangular prisms, in which the size of the prisms are kept fixed during the inversion and the values of densities of the prisms are the mode...

The recently-developed unsupervised graph representation learning approaches apply contrastive into graph-structured data and achieve promising performance. However, these methods mainly focus on augmentation for positive samples, while the negative mining strategies are less explored, leading to sub-optimal To tackle this issue, we propose a Graph Adversarial Contrastive Learning (GraphACL) sc...

In genomic analysis, there is growing interest in network structures that represent biochemistry interactions. Graph structured or constrained inference takes advantage of a known relational structure among variables to introduce smoothness and reduce complexity in modeling, especially for high-dimensional genomic data. There has been a lot of interest in its application in model regularization...

The Universum data, defined as a collection of ”nonexamples” that do not belong to any class of interest, have been shown to encode some prior knowledge by representing meaningful concepts in the same domain as the problem at hand. In this paper, we address a novel semi-supervised classification problem, called semi-supervised Universum, that can simultaneously utilize the labeled data, unlabel...