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学者姓名：郭文忠
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Abstract ：
Multi-view learning has demonstrated strong potential in processing data from different sources or viewpoints. Despite the significant progress made by Multi-view Graph Neural Networks (MvGNNs) in exploiting graph structures, features, and representations, existing research generally lacks architectures specifically designed for the intrinsic properties of multi-view data. This leads to models that still have deficiencies in fully utilizing consistent and complementary information in multi-view data. Most of current research tends to simply extend the single-view GNN framework to multi-view data, lacking in-depth strategies to handle and leverage the unique properties of these data. To address this issue, we propose a simple yet effective MvGNN framework called Multi-view Representation Learning with Decoupled private and shared Propagation (MvRL-DP). This framework enables multi-view data to be effectively processed as a whole by alternating private and shared operations to integrate cross-view information. In addition, to address possible inconsistencies between views, we present a discriminative loss that promotes class separability and prevents the model from being misled by noise hidden in multi-view data. Experiments demonstrate that the proposed framework is superior to current state-of-the-art methods in the multi-view semi-supervised classification task.
Keyword ：
Multi-view learning Multi-view learning Propagation decoupling Propagation decoupling Representation learning Representation learning Semi-supervised classification Semi-supervised classification Tensor operation Tensor operation
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| GB/T 7714 | Wang, Xuzheng , Lan, Shiyang , Wu, Zhihao et al. Multi-view Representation Learning with Decoupled private and shared Propagation [J]. | KNOWLEDGE-BASED SYSTEMS , 2025 , 310 . |
| MLA | Wang, Xuzheng et al. "Multi-view Representation Learning with Decoupled private and shared Propagation" . | KNOWLEDGE-BASED SYSTEMS 310 (2025) . |
| APA | Wang, Xuzheng , Lan, Shiyang , Wu, Zhihao , Guo, Wenzhong , Wang, Shiping . Multi-view Representation Learning with Decoupled private and shared Propagation . | KNOWLEDGE-BASED SYSTEMS , 2025 , 310 . |
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Asa promising area in machine learning, multi-view learning enhances model performance by integrating data from various views. With the rise of graph convolutional networks, many studies have explored incorporating them into multi-view learning frameworks. However, these methods often require storing the entire graph topology, leading to significant memory demands. Additionally, iterative update operations in graph convolutions lead to longer inference times, making it difficult to deploy existing multi-view learning models on large graphs. To overcome these challenges, we introduce an efficient multi-view graph convolutional network via local aggregation and global propagation. In the local aggregation module, we use a structure-aware matrix for feature aggregation, which significantly reduces computational complexity compared to traditional graph convolutions. After that, we design a global propagation module that allows the model to be trained in batches, enabling deployment on large-scale graphs. Finally, we introduce the attention mechanism into multi-view feature fusion to more effectively explore the consistency and complementarity between views. The proposed method is employed to perform multi-view semi-supervised classification, and comprehensive experimental results on benchmark datasets validate its effectiveness.
Keyword ：
Graph neural networks Graph neural networks Local aggregation Local aggregation Multi-view learning Multi-view learning Representation learning Representation learning Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Liu, Lu , Shi, Yongquan , Pi, Yueyang et al. Efficient multi-view graph convolutional networks via local aggregation and global propagation [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 266 . |
| MLA | Liu, Lu et al. "Efficient multi-view graph convolutional networks via local aggregation and global propagation" . | EXPERT SYSTEMS WITH APPLICATIONS 266 (2025) . |
| APA | Liu, Lu , Shi, Yongquan , Pi, Yueyang , Guo, Wenzhong , Wang, Shiping . Efficient multi-view graph convolutional networks via local aggregation and global propagation . | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 266 . |
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Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most objects in the real world often have underlying connections, organizing multi-view data as heterogeneous graphs is beneficial to extracting latent information among different objects. Due to the powerful capability to gather information of neighborhood nodes, in this article, we apply Graph Convolutional Network (GCN) to cope with heterogeneous graph data originating from multi-view data, which is still under-explored in the field of GCN. In order to improve the quality of network topology and alleviate the interference of noises yielded by graph fusion, some methods undertake sorting operations before the graph convolution procedure. These GCN-based methods generally sort and select the most confident neighborhood nodes for each vertex, such as picking the top-k nodes according to pre-defined confidence values. Nonetheless, this is problematic due to the non-differentiable sorting operators and inflexible graph embedding learning, which may result in blocked gradient computations and undesired performance. To cope with these issues, we propose a joint framework dubbed Multi-view Graph Convolutional Network with Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive graph fusion layer, a graph learning module, and a differentiable node selection schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims to learn more robust graph fusion through a differentiable neural network. The effectiveness of the proposed method is verified by rigorous comparisons with considerable state-of-the-art approaches in terms of multi-view semi-supervised classification tasks, and the experimental results indicate that MGCN-DNS achieves pleasurable performance on several benchmark multi-view datasets.
Keyword ：
differentiable node selection differentiable node selection graph convolutional network graph convolutional network Multi-view learning Multi-view learning semi-supervised classification semi-supervised classification
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| GB/T 7714 | Chen, Zhaoliang , Fu, Lele , Xiao, Shunxin et al. Multi-View Graph Convolutional Networks with Differentiable Node Selection [J]. | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2024 , 18 (1) . |
| MLA | Chen, Zhaoliang et al. "Multi-View Graph Convolutional Networks with Differentiable Node Selection" . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 18 . 1 (2024) . |
| APA | Chen, Zhaoliang , Fu, Lele , Xiao, Shunxin , Wang, Shiping , Plant, Claudia , Guo, Wenzhong . Multi-View Graph Convolutional Networks with Differentiable Node Selection . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2024 , 18 (1) . |
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Deep learning-based clustering methods, especially those incorporating deep generative models, have recently shown noticeable improvement on many multimedia benchmark datasets. However, existing generative models still suffer from unstable training, and the gradient vanishes, which results in the inability to learn desirable embedded features for clustering. In this paper, we aim to tackle this problem by exploring the capability of Wasserstein embedding in learning representative embedded features and introducing a new clustering module for jointly optimizing embedding learning and clustering. To this end, we propose Wasserstein embedding clustering (WEC), which integrates robust generative models with clustering. By directly minimizing the discrepancy between the prior and marginal distribution, we transform the optimization problem of Wasserstein distance from the original data space into embedding space, which differs from other generative approaches that optimize in the original data space. Consequently, it naturally allows us to construct a joint optimization framework with the designed clustering module in the embedding layer. Due to the substitutability of the penalty term in Wasserstein embedding, we further propose two types of deep clustering models by selecting different penalty terms. Comparative experiments conducted on nine publicly available multimedia datasets with several state-of-the-art methods demonstrate the effectiveness of our method.
Keyword ：
auto-encoder auto-encoder clustering analysis clustering analysis Clustering methods Clustering methods Data models Data models Decoding Decoding Deep learning Deep learning Generative adversarial networks Generative adversarial networks generative models generative models Task analysis Task analysis Training Training Unsupervised learning Unsupervised learning Wasserstein embedding Wasserstein embedding
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| GB/T 7714 | Cai, Jinyu , Zhang, Yunhe , Wang, Shiping et al. Wasserstein Embedding Learning for Deep Clustering: A Generative Approach [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 7567-7580 . |
| MLA | Cai, Jinyu et al. "Wasserstein Embedding Learning for Deep Clustering: A Generative Approach" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 7567-7580 . |
| APA | Cai, Jinyu , Zhang, Yunhe , Wang, Shiping , Fan, Jicong , Guo, Wenzhong . Wasserstein Embedding Learning for Deep Clustering: A Generative Approach . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 7567-7580 . |
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时空动作检测依赖于视频空间信息与时间信息的学习. 目前，最先进的基于卷积神经网络的动作检测器采用2D CNN或3D CNN架构，取得了显著的效果. 然而，由于网络结构的复杂性与时空信息感知的原因，这些方法通常采用非实时、离线的方式. 时空动作检测主要的挑战在于设计高效的检测网络架构，并能有效地感知融合时空特征. 考虑到上述问题，本文提出了一种基于时空交叉感知的实时动作检测方法. 该方法首先通过对输入视频进行乱序重排来增强时序信息，针对仅使用2D或3D骨干网络无法有效对时空特征进行建模，提出了基于时空交叉感知的多分支特征提取网络. 针对单一尺度时空特征描述性不足，提出一个多尺度注意力网络来学习长期的时间依赖和空间上下文信息. 针对时序和空间两种不同来源特征的融合，提出了一种新的运动显著性增强融合策略，对时空信息进行编码交叉映射，引导时序特征和空间特征之间的融合，突出更具辨别力的时空特征表示. 最后，基于帧级检测器结果在线计算动作关联性链接 . 本文提出的方法在两个时空动作数据集 UCF101-24 和 JHMDB-21 上分别达到了 84.71% 和78.4%的准确率，优于现有最先进的方法，并达到 73帧/秒的速度 . 此外，针对 JHMDB-21数据集存在高类间相似性与难样本数据易于混淆等问题，本文提出了基于动作表示的关键帧光流动作检测方法，避免了冗余光流的产生，进一步提升了动作检测准确率.
Keyword ：
多尺度注意力 多尺度注意力 实时动作检测 实时动作检测 时空交叉感知 时空交叉感知
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| GB/T 7714 | 柯逍 , 缪欣 , 郭文忠 . 基于时空交叉感知的实时动作检测方法 [J]. | 电子学报 , 2024 . |
| MLA | 柯逍 et al. "基于时空交叉感知的实时动作检测方法" . | 电子学报 (2024) . |
| APA | 柯逍 , 缪欣 , 郭文忠 . 基于时空交叉感知的实时动作检测方法 . | 电子学报 , 2024 . |
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Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi -relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta -paths, which significantly impact the quality of learned embeddings. In this paper, we propose an Attributed Multi -Order Graph Convolutional Network (AMOGCN), which automatically explores meta -paths that involve multi -hop neighbors by aggregating multi -order adjacency matrices. The proposed model first constructs different orders of adjacency matrices from manually designed node connections. Next, AMOGCN fuses these various orders of adjacency matrices to create an intact multi -order adjacency matrix. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we employ a one -layer simplifying graph convolutional network with the learned multi -order adjacency matrix, which is equivalent to the cross -hop node information propagation with multilayer graph neural networks. Substantial experiments reveal that AMOGCN achieves superior semi -supervised classification performance compared with state-of-the-art competitors.
Keyword ：
Graph convolutional networks Graph convolutional networks Heterogeneous graphs Heterogeneous graphs Multi-order adjacency matrix Multi-order adjacency matrix Semi-supervised classification Semi-supervised classification
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| GB/T 7714 | Chen, Zhaoliang , Wu, Zhihao , Zhong, Luying et al. Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs [J]. | NEURAL NETWORKS , 2024 , 174 . |
| MLA | Chen, Zhaoliang et al. "Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs" . | NEURAL NETWORKS 174 (2024) . |
| APA | Chen, Zhaoliang , Wu, Zhihao , Zhong, Luying , Plant, Claudia , Wang, Shiping , Guo, Wenzhong . Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs . | NEURAL NETWORKS , 2024 , 174 . |
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Research on knowledge graph embedding (KGE) has emerged as an active field in which most existing KGE approaches mainly focus on static structural data and ignore the influence of temporal variation involved in time -aware triples. In order to deal with this issue, several temporal knowledge graph embedding (TKGE) approaches have been proposed to integrate temporal and structural information. However, these methods only employ a uniformly random sampling to construct negative facts. As a consequence, the corrupted samples are often too simplistic for training an effective model. In this paper, we propose a new temporal knowledge graph embedding framework by introducing adversarial learning to further refine the performance of traditional TKGE models. In our framework, a generator is utilized to construct high -quality plausible quadruples and a discriminator learns to obtain the embeddings of entities and relations based on both positive and negative samples. Meanwhile, we also apply a Gumbel-Softmax relaxation and the Wasserstein distance to prevent vanishing gradient problems on discrete data; an inherent flaw in traditional generative adversarial networks. Through comprehensive experimentation on temporal datasets, the results indicate that our proposed framework can attain significant improvements based on benchmark models and also demonstrate the effectiveness and applicability of our framework.
Keyword ：
Generative adversarial networks Generative adversarial networks Gumbel-Softmax relaxation Gumbel-Softmax relaxation Temporal knowledge graph embedding Temporal knowledge graph embedding Wasserstein distance Wasserstein distance
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| GB/T 7714 | Dai, Yuanfei , Guo, Wenzhong , Eickhoff, Carsten . Wasserstein adversarial learning based temporal knowledge graph embedding [J]. | INFORMATION SCIENCES , 2024 , 659 . |
| MLA | Dai, Yuanfei et al. "Wasserstein adversarial learning based temporal knowledge graph embedding" . | INFORMATION SCIENCES 659 (2024) . |
| APA | Dai, Yuanfei , Guo, Wenzhong , Eickhoff, Carsten . Wasserstein adversarial learning based temporal knowledge graph embedding . | INFORMATION SCIENCES , 2024 , 659 . |
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Recent developments in the field of non-local attention (NLA) have led to a renewed interest in self-similarity-based single image super-resolution (SISR). Researchers usually use the NLA to explore non-local self-similarity (NSS) in SISR and achieve satisfactory reconstruction results. However, a surprising phenomenon that the reconstruction performance of the standard NLA is similar to that of the NLA with randomly selected regions prompted us to revisit NLA. In this paper, we first analyzed the attention map of the standard NLA from different perspectives and discovered that the resulting probability distribution always has full support for every local feature, which implies a statistical waste of assigning values to irrelevant non-local features, especially for SISR which needs to model long-range dependence with a large number of redundant non-local features. Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution. Furthermore, we derived some key properties of the soft thresholding operation that enable training our HSPA in an end-to-end manner. The HSPA can be integrated into existing deep SISR models as an efficient general building block. In addition, to demonstrate the effectiveness of the HSPA, we constructed a deep high-similarity-pass attention network (HSPAN) by integrating a few HSPAs in a simple backbone. Extensive experimental results demonstrate that HSPAN outperforms state-of-the-art approaches on both quantitative and qualitative evaluations. Our code and a pre-trained model were uploaded to GitHub (https://github.com/laoyangui/HSPAN) for validation.
Keyword ：
deep learning deep learning High-similarity-pass attention High-similarity-pass attention single image super-resolution single image super-resolution softmax transformation softmax transformation
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| GB/T 7714 | Su, Jian-Nan , Gan, Min , Chen, Guang-Yong et al. High-Similarity-Pass Attention for Single Image Super-Resolution [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2024 , 33 : 610-624 . |
| MLA | Su, Jian-Nan et al. "High-Similarity-Pass Attention for Single Image Super-Resolution" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 33 (2024) : 610-624 . |
| APA | Su, Jian-Nan , Gan, Min , Chen, Guang-Yong , Guo, Wenzhong , Chen, C. L. Philip . High-Similarity-Pass Attention for Single Image Super-Resolution . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2024 , 33 , 610-624 . |
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SMT is an optimized model for solving the routing problem of a multipin net in very large-scale integrated circuits. As the appearance of various obstacles on chips, the obstacle-avoiding problem has attracted much attention in recent years. Meanwhile, since interconnect delay plays a major role in chip delay, timing analysis is another critical problem worthy of consideration when constructing an Steiner minimum tree (SMT). Furthermore, the introduction of the X-architecture allows for better utilization of routing resources. In this article, a timing-driven obstacle-avoiding X-architecture Steiner minimum tree algorithm with slack constraints (TD-OAXSMT-SC) is proposed to consider obstacle-avoiding, timing slack constraints, and X-architecture simultaneously for the first time. The TD-OAXSMT-SC algorithm consists of four major stages: 1) in the routing tree initialization stage, this article constructs an X-architecture Prim-Dijkstra spanning tree as the initial routing tree with minimum total delay; 2) in the particle swarm optimization (PSO)-based routing tree iteration stage, a novel discrete PSO algorithm based on genetic operators is proposed to obtain a high-quality routing tree; 3) in the routing tree standardization stage, two effective standardization strategies are proposed to obtain a routing tree that satisfies both obstacle-avoiding and timing slack constraints; and 4) in the routing tree optimization stage, the connection of interconnected wires is optimized in a global manner, thus obtaining an optimized routing tree. Experimental results show that the proposed TD-OAXSMT-SC algorithm outperforms the state-of-the-art methods in routing quality with slack constraints.
Keyword ：
Delays Delays Integrated circuit interconnections Integrated circuit interconnections Obstacle-avoiding Obstacle-avoiding Optimization Optimization Pins Pins PSO PSO Routing Routing SMT SMT timing-driven routing timing-driven routing timing slack constraints timing slack constraints Very large scale integration Very large scale integration Wires Wires X-architecture routing X-architecture routing
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| GB/T 7714 | Zhu, Yuhan , Liu, Genggeng , Lu, Ren et al. Timing-Driven Obstacle-Avoiding X-Architecture Steiner Minimum Tree Algorithm With Slack Constraints [J]. | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 , 54 (5) : 2927-2940 . |
| MLA | Zhu, Yuhan et al. "Timing-Driven Obstacle-Avoiding X-Architecture Steiner Minimum Tree Algorithm With Slack Constraints" . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 54 . 5 (2024) : 2927-2940 . |
| APA | Zhu, Yuhan , Liu, Genggeng , Lu, Ren , Huang, Xing , Gan, Min , Guo, Wenzhong . Timing-Driven Obstacle-Avoiding X-Architecture Steiner Minimum Tree Algorithm With Slack Constraints . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 , 54 (5) , 2927-2940 . |
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Robust nonlinear regression frequently arises in data analysis that is affected by outliers in various application fields such as system identification, signal processing, and machine learning. However, it is still quite challenge to design an efficient algorithm for such problems due to the nonlinearity and nonsmoothness. Previous researches usually ignore the underlying structure presenting in the such nonlinear regression models, where the variables can be partitioned into a linear part and a nonlinear part. Inspired by the high efficiency of variable projection algorithm for solving separable nonlinear least squares problems, in this paper, we develop a robust variable projection (RoVP) method for the parameter estimation of separable nonlinear regression problem with $L_{1}$ norm loss. The proposed algorithm eliminates the linear parameters by solving a linear programming subproblem, resulting in a reduced problem that only involves nonlinear parameters. More importantly, we derive the Jacobian matrix of the reduced objective function, which tackles the coupling between the linear parameters and nonlinear parameters. Furthermore, we observed an intriguing phenomenon in the landscape of the original separable nonlinear objective function, where some narrow valleys frequently exist. The RoVP strategy can effectively diminish the likelihood of the algorithm getting stuck in these valleys and accelerate its convergence. Numerical experiments confirm the effectiveness and robustness of the proposed algorithm. IEEE
Keyword ：
Autoregressive processes Autoregressive processes Jacobian matrices Jacobian matrices Linear programming Linear programming Optimization Optimization Parameter estimation Parameter estimation Predictive models Predictive models radial basis function network based autoregressive (RBF-AR) model radial basis function network based autoregressive (RBF-AR) model robust parameter estimation robust parameter estimation Signal processing algorithms Signal processing algorithms System identification System identification variable projection (VP) algorithm variable projection (VP) algorithm
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| GB/T 7714 | Chen, G. , Su, X. , Gan, M. et al. Robust variable projection algorithm for the identification of separable nonlinear models [J]. | IEEE Transactions on Automatic Control , 2024 , 69 (9) : 1-8 . |
| MLA | Chen, G. et al. "Robust variable projection algorithm for the identification of separable nonlinear models" . | IEEE Transactions on Automatic Control 69 . 9 (2024) : 1-8 . |
| APA | Chen, G. , Su, X. , Gan, M. , Guo, W. , Chen, C.L.P. . Robust variable projection algorithm for the identification of separable nonlinear models . | IEEE Transactions on Automatic Control , 2024 , 69 (9) , 1-8 . |
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