Graph Convolutional Networks (GCNs) have received a lot of attention in pattern recognition and machine learning. In this paper, we present revisited scheme for the new method called ”GCNs with Manifold Regularization” (GCNMR). While manifold regularization can add additional information, GCN-based semi-supervised classification process cannot consider full layer-wise structured information. In...

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...

The main idea behind graph-based semi–supervised learning is to use pair–wise similarities between data instances to enhance classification accuracy (see (Zhu, 2005) for a survey of existing approaches). Many graph–based techniques use certain type of regularization that often involve a graph Laplacian operator (e.g., see (Belkin et al., 2006)). Intuitively, this corresponds to a diffusion proc...

In this paper we provide a principled approach to solve a transductive classification problem involving a similar graph (edges tend to connect nodes with same labels) and a dissimilar graph (edges tend to connect nodes with opposing labels). Most of the existing methods, e.g., Information Regularization (IR), Weighted vote Relational Neighbor classifier (WvRN) etc, assume that the given graph i...

We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions. We estimate the linear weights to learn the effective kernel function by appropriate regularization based on graph smoothness. We s...

A novel framework called Graph diffusion & PCA (GDPCA) is proposed in the context of semi-supervised learning on graph structured data. It combines a modified Principal Component Analysis with classical supervised loss and Laplacian regularization, thus handling case where adjacency matrix Sparse avoiding Curse dimensionality. Our can be applied to non-graph datasets as well, such images by con...

Abstract: Sparse models for high-dimensional linear regression and machine learning have received substantial attention over the past two decades. Model selection, or determining which features or covariates are the best explanatory variables, is critical to the interpretability of a learned model. Much of the current literature assumes that covariates are only mildly correlated. However, in mo...

Domain adaptation, which aims to learn domain-invariant features for sentiment classification, has received increasing attention. The underlying rationality of domain adaptation is that the involved domains share some common latent factors. Recently neural network based on Stacked Denoising Auto-Encoders (SDA) and its marginalized version (mSDA) have shown promising results on learning domain-i...

Labeling of discrete Markov Random Fields (MRFs) has become an attractive approach for solving the problem of non-rigid image registration. Here, regularization plays an important role in order to obtain smooth deformations for the inherent ill-posed problem. Smoothness is achieved by penalizing the derivatives of the displacement field. However, efficient optimization strategies (based on iter...