Multi-view Graph Research Articles

Relation Extraction for Chinese Clinical Records Using Multi-View Graph Learning

Relation extraction is a necessary step in obtaining information from clinical medical records. In the medical domain, there have been several studies on relation extraction in modern medicine clinical notes written in English. However, very limited relation extraction research has been conducted on clinical notes written in Chinese, especially traditional Chinese medicine (TCM) clinical records (e.g., herb-symptom, herb-disease). Instead of independently extracting each relation from a single sentence or text, we propose to globally and reasonably extract multiple types of relations from the Chines clinical records with a novel heterogeneous graph representation learning method. Specifically, we first construct multiple view medical entity graphs based on the co-occurring relations, knowledge obtained from the clinic, and domain texts with the corresponding information of two medical entities from the Chinese clinical records, in which each edge is a candidate relation; we then build a Graph Convolutional Network (GCN)-based representation learning with the attention mechanism to simultaneously infer the existence of all the edges via classification. The experimental data were obtained from the Chinese medical records and literature provided by previous work. The main experimental results on Chinese clinical records show that our proposed model’s precision, recall, and F1-score reach 10.2%, 13.5%, 12.6%, demonstrating significant improvements over state-of-the-art.

MV-GNN: Multi-View Graph Neural Network for Compression Artifacts Reduction

Inevitable compression artifacts in multi-view video (MVV) can clearly degrade the quality of experience in many interaction-oriented 3D visual applications. Under the framework of asymmetric coding, low-quality images can be enhanced with high-quality images from the neighboring viewpoints considering the similarity among different views. However, compression artifacts and warping error cause different cross-view quality gaps for various sequences, and thus the contribution of cross-view priors can hardly be located and considered in previous works. In this paper, we propose a multi-view graph neural network (MV-GNN) to reduce compression artifacts in multi-view compressed images. We dedicate to design a fusion mechanism which can exploit contributions from neighboring viewpoints and meanwhile suppress the misleading information. In our method, a GNN-based fusion mechanism is designed to fuse the cross-view information under the aggregation and update mechanism of GNN. Experiments show that 1.672 dB and 0.0242 average gains on PSNR and SSIM metrics can be obtained, respectively. For the subjective evaluations, blocking effect in the compressed images are clearly suppressed and the damaged object boundary are better recovered. The experimental results demonstrate that our MV-GNN outperforms the state-of-the-art methods.

Clustering Structure-Induced Robust Multi-View Graph Recovery

Graph based classification methods have been widely applied in the fields of computer vision and machine learning. The quality of the graph highly affects the performance of these methods. The same object is commonly represented by different features, i.e., multi-view features, which leads to multiple graphs corresponding to different features in multi-view learning. However, what kind of graph is important for the task is unknown in advance. Moreover, existing multi-view learning methods become weak in dealing with noisy graphs when the data is corrupted by the noise. In this paper, we address this problem by observing that the noise of each graph has specific structure. Then, based on this observation we propose a robust multi-view graph recovery (RMGR) method in which the specific structure is used to clean the multiple input noisy graphs and these cleaned graphs are simultaneously aggregated into a consensus graph by adaptively assigning great weighted coefficients for important graphs. To make the consensus graph suit classification, the clustering structure is introduced to restrain the rank of Laplacian matrix of the consensus graph such that the number of its connected components is equal to that of clustering. In doing so, the graph is adaptively adjusted during optimization to more accurately partition data. The optimization problem is solved by proposed the iterative update algorithm. Extensive experiments on synthetic and several benchmark data sets show the effectiveness of the proposed method.

Object Detection Using Multiview CCA-Based Graph Spectral Learning

Recent years have witnessed a surge of interest in semi-supervised learning-based object detection. Object detection is usually accomplished by classification methods. Different from conventional methods, those usually adopt a single feature view or concatenate multiple features into a long feature vector, multiview graph spectral learning can attain simultaneously object classification and weight learning of multiview. However, most existing multiview graph spectral learning (GSL) methods are only concerned with the complementarities between multiple views but not with correlation information. Accurately representing image objects is difficult because there are multiple views simultaneously for an image object. Thus, we offer a GSL method based on multiview canonical correlation analysis (GSL-MCCA). The method adds MCCA regularization term to a graph learning framework. To enable MCCA to reveal the nonlinear correlation information hidden in multiview data, manifold local structure information is incorporated into MCCA. Thus, GSL-MCCA can lead to simultaneous selection of relevant features and learning transformation. Experimental evaluations based on Corel and VOC datasets suggest the effectiveness of GSL-MCCA in object detection.

Saliency detection via multi-view graph based saliency optimization

Saliency detection is an important problem in computer vision and pattern recognition area. Many works have been proposed for addressing the saliency detection task. As a popular method, graph based saliency optimization has been widely studied. However, previous works have universally focussed on single graph optimization which fails to consider multi-view feature representation of image content. In this paper, we first provide a general framework for traditional graph based saliency optimization models. Then, we extend the general framework to the multi-view case and propose our general multi-view graph based saliency optimization model. Finally, we present a particular implementation of our general model and derive an effective updating algorithm to solve it. Experimental results using several benchmark datasets demonstrate the effectiveness of our proposed saliency model.

MV-GCN: Multi-View Graph Convolutional Networks for Link Prediction

Link prediction is a demanding task in real-world scenarios, such as recommender systems, which targets to predict the unobservable links between different objects by learning network-structured data. In this paper, we propose a novel multi-view graph convolutional neural network (MV-GCN) model to solve this problem based on Matrix Completion method by simultaneously exploiting the interactive relationship and the content information of different objects. Unlike existing approaches directly concatenate the interactive and content information as a single view, the proposed MV-GCN improves the accuracy of the predictions by restricting the consistencies on the graph embedding from multiple views. Experimental results on six primary benchmark datasets, including two homogeneous datasets and four heterogeneous datasets, both show that MV-GCN outperforms the recent state-of-the-art methods.

Multi-view predictive latent space learning

In unsupervised circumstances, multi-view learning seeks a shared latent representation by taking the consensus and complementary principles into account. However, most existing multi-view unsupervised learning approaches do not explicitly lay stress on the predictability of the latent space. In this paper, we propose a novel multi-view predictive latent space learning (MVP) model and apply it to multi-view clustering and unsupervised dimension reduction. The latent space is forced to be predictive by maximizing the correlation between the latent space and feature space of each view. By learning a multi-view graph with adaptive view-weight learning, MVP effectively combines the complementary information from multi-view data. Experimental results on benchmark datasets show that MVP outperforms the state-of-the-art multi-view clustering and unsupervised dimension reduction algorithms.

Multi-view clustering: A survey

In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views, while fusing these data. Multi-view Clustering (MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets. Overall, this paper serves as an introductory text and survey for multi-view clustering.

Generating Storyline with Societal Risk from Tianya Club

AbstractMajor societal problems affect the social stability. It is necessary to understand the public opinion toward those issues to avoid social conflicts. Nowadays the social media become the major platform to track what the public is concerned about and which may be of the societal risk. However, it is very tough to capture the public attention in short time due to huge flow of user-generated contents. In this paper, we approach this problem by expanding the method of generating storyline with the result displayed by a multi-view graph. One real-world example is illustrated and evaluation is given to show the effectiveness of the proposed method.

Discriminative metric learning for multi-view graph partitioning

In recent years, multi-view graph partitioning has attracted more and more attention, but most efforts have been made to develop graph partitioning approaches directly in the original topological structure. In many real-world applications, graph may contain noisy links and the distance metric may not be so discriminative for revealing cluster structure. This paper addresses the problem of discriminative metric learning for multi-view graph partitioning. In particular, we propose a novel method called Multi-view DML (abbr. of Multi-view Discriminative Metric Learning) to transform the metric space in the original graph into a more discriminative metric space, in which better graph partitioning results will be obtained. We envision the multi-view graph as an adaptive dynamic system, where both the intra-view connections and the inter-view couplings are interplayed to gradually update the relation metric among nodes. On the one hand, the inter-view coupling will be influenced by the intra-view connection between two nodes. On the other hand, the inter-view coupling will also affect the intra-view connection. Such interplay eventually makes the whole graph reach a steady state which has a stronger cluster structure than the original graph. Extensive experiments are conducted on both synthetic and real-world graphs to confirm that the proposed method is able to learn more discriminative metric.

Image annotation using multi-view non-negative matrix factorization with different number of basis vectors

Automatic Image Annotation (AIA) helps image retrieval systems by predicting tags for images. In this paper, we propose an AIA system using Non-negative Matrix Factorization (NMF) framework. The NMF framework discovers a latent space, by factorizing data into a set of non-negative basis and coefficients. To model the images, multiple features are extracted, each one represents images from a specific view. We use multi-view graph regularization NMF and allow NMF to choose a different number of basis vectors for each view. For tag prediction, each test image is mapped onto the multiple latent spaces. The distances of images in these spaces are used to form a unified distance matrix. The weights of distances are learned automatically. Then a search-based method is used to predict tags based on tags of nearest neighbors’. We evaluate our method on three datasets and show that it is competitive with the current state-of-the-art methods.

Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning.

In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (MIL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse -graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the MIL. Experiments and analyses in many practical applications prove the effectiveness of the M IL.

Multiple Structure-View Learning for Graph Classification.

Many applications involve objects containing structure and rich content information, each describing different feature aspects of the object. Graph learning and classification is a common tool for handling such objects. To date, existing graph classification has been limited to the single-graph setting with each object being represented as one graph from a single structure-view. This inherently limits its use to the classification of complicated objects containing complex structures and uncertain labels. In this paper, we advance graph classification to handle multigraph learning for complicated objects from multiple structure views, where each object is represented as a bag containing several graphs and the label is only available for each graph bag but not individual graphs inside the bag. To learn such graph classification models, we propose a multistructure-view bag constrained learning (MSVBL) algorithm, which aims to explore substructure features across multiple structure views for learning. By enabling joint regularization across multiple structure views and enforcing labeling constraints at the bag and graph levels, MSVBL is able to discover the most effective substructure features across all structure views. Experiments and comparisons on real-world data sets validate and demonstrate the superior performance of MSVBL in representing complicated objects as multigraph for classification, e.g., MSVBL outperforms the state-of-the-art multiview graph classification and multiview multi-instance learning approaches.

CGC: A Flexible and Robust Approach to Integrating Co-Regularized Multi-Domain Graph for Clustering.

Multi-view graph clustering aims to enhance clustering performance by integrating heterogeneous information collected in different domains. Each domain provides a different view of the data instances. Leveraging cross-domain information has been demonstrated an effective way to achieve better clustering results. Despite the previous success, existing multi-view graph clustering methods usually assume that different views are available for the same set of instances. Thus instances in different domains can be treated as having strict one-to-one relationship. In many real-life applications, however, data instances in one domain may correspond to multiple instances in another domain. Moreover, relationships between instances in different domains may be associated with weights based on prior (partial) knowledge. In this paper, we propose a flexible and robust framework, CGC (Co-regularized Graph Clustering), based on non-negative matrix factorization (NMF), to tackle these challenges. CGC has several advantages over the existing methods. First, it supports many-to-many cross-domain instance relationship. Second, it incorporates weight on cross-domain relationship. Third, it allows partial cross-domain mapping so that graphs in different domains may have different sizes. Finally, it provides users with the extent to which the cross-domain instance relationship violates the in-domain clustering structure, and thus enables users to re-evaluate the consistency of the relationship. We develop an efficient optimization method that guarantees to find the global optimal solution with a given confidence requirement. The proposed method can automatically identify noisy domains and assign smaller weights to them. This helps to obtain optimal graph partition for the focused domain. Extensive experimental results on UCI benchmark data sets, newsgroup data sets and biological interaction networks demonstrate the effectiveness of our approach.

MVGS: A new graph signature for self-reconfiguration planning of modular robots based on Multiple Views Theory

Self-reconfiguration planning (SRP) of modular robots has been a major research for the past decade and still is a key issue. In this paper, we present a novel new graph signature approach for SRP based on Multiple Views Theory, called MVGS (Multi-View Graph Signature). In the proposed method the time complexity of the graph signature generation reduced to O(logn), in which n is the number of modules, from the best solutions with O(n2). Also, we propose a new similarity metric, between graph signatures to better guide the heuristic search toward the final configuration. The proposed approach has been implemented in a simulator and the results show significantly better performance than the other previously proposed methods.