A Quantum Spatial Graph Convolutional Network for Text Classification

The data generated from non-Euclidean domains and its graphical representation(with complex-relationship object interdependence)applications has observed an exponential growth.The sophistication of graph data has posed consequential obstacles to the existing machine learning algorithms.In this study,we have considered a revamped version of a semi-supervised learning algorithm for graph-structured data to address the issue of expanding deep learning approaches to represent the graph data.Additionally,the quantum information theory has been applied through Graph Neural Networks(GNNs)to generate Riemannian metrics in closed-form of several graph layers.In further,to pre-process the adjacency matrix of graphs,a new formulation is established to incorporate high order proximities.The proposed scheme has shown outstanding improvements to overcome the deficiencies in Graph Convolutional Network(GCN),particularly,the information loss and imprecise information representation with acceptable computational overhead.Moreover,the proposed Quantum Graph Convolutional Network(QGCN)has significantly strengthened the GCN on semi-supervised node classification tasks.In parallel,it expands the generalization process with a significant difference by making small random perturbations
G of the graph during the training process.The evaluation results are provided on three benchmark datasets,including Citeseer,Cora,and PubMed,that distinctly delineate the superiority of the proposed model in terms of computational accuracy against state-of-the-art GCN and three other methods based on the same algorithms in the existing literature.

自适应样本加权的多视图聚类算法

将原始数据投影到一个包含几何一致性和簇分配一致性的空间,并且可以自适应学习几乎所有参数的多视图聚类算法,能够获得良好的聚类效果,但这样做没有考虑多视图中不同样本重要性不同的特点,忽视了噪声点与离群点对聚类效果造成的不利影响.针对上述问题,对样本重要性进行研究,提出一种自适应样本加权的多视图聚类算法.该算法对视图中不同的样本根据其重要性进行加权处理:首先给每个样本分配相同的权重,在之后的每次迭代中,不断进行自适应调整直至达到收敛条件.实验结果表明,该算法可以获得更好的实验效果.

Unpaired image to image transformation via informative coupled generative adversarial networks

We consider image transformation problems,and the objective is to translate images from a source domain to a target one.The problem is challenging since it is difficult to preserve the key properties of the source images,and to make the details of target being as distinguishable as possible.To solve this problem,we propose an informative coupled generative adversarial networks(ICoGAN).For each domain,an adversarial generator-and-discriminator network is constructed.Basically,we make an approximately-shared latent space assumption by a mutual information mechanism,which enables the algorithm to learn representations of both domains in unsupervised setting,and to transform the key properties of images from source to target.Moreover,to further enhance the performance,a weightsharing constraint between two subnetworks,and different level perceptual losses extracted from the intermediate layers of the networks are combined.With quantitative and visual results presented on the tasks of edge to photo transformation,face attribute transfer,and image inpainting,we demonstrate the ICo-GAN’s effectiveness,as compared with other state-of-the-art algorithms.

Non-negative matrix factorization based modeling and training algorithm for multi-label learning

Multi-label learning is more complicated than single-label learning since the semantics of the instances are usually overlapped and not identical.The effectiveness of many algorithms often fails when the correlations in the feature and label space are not fully exploited.To this end,we propose a novel non-negative matrix factorization(NMF)based modeling and training algorithm that learns from both the adjacencies of the instances and the labels of the training set.In the modeling process,a set of generators are constructed,and the associations among generators,instances,and labels are set up,with which the label prediction is conducted.In the training process,the parameters involved in the process of modeling are determined.Specifically,an NMF based algorithm is proposed to determine the associations between generators and instances,and a non-negative least square optimization algorithm is applied to determine the associations between generators and labels.The proposed algorithm fully takes the advantage of smoothness assumption,so that the labels are properly propagated.The experiments were carried out on six set of benchmarks.The results demonstrate the effectiveness of the proposed algorithms.