The compact layout and complex background of printed circuit boards (PCBs) pose significant challenges for surface defect detection. With limited computational resources, ensuring that PCB defect detection models are lightweight while maintaining detection performance is a persistent challenge. To address this, we propose a novel GS-YOLO network based on the YOLOv5s framework, designed to accurately identify PCB surface defects with lower computational cost. In the GS-YOLO model, we introduce the C3Ghost-S module, integrating it into the network’s backbone and neck structures. This not only reduces computational cost but also improves the model’s detection accuracy and generalization ability. Additionally, we propose the GA-SPPF module, which extracts global information and combines it with local information obtained from the SPPF module, helping the model learn both local and global features for a more comprehensive understanding of the image. Notably, the modules we designed are plug-and-play within YOLO architecture series, allowing for easy integration into different YOLO-based frameworks. Compared to YOLOv5s, GS-YOLO improves mAP by 8.1 %, reduces the parameter count by 26.49 %, and lowers computational complexity by 32.27 %. To further validate the model’s generalization capability, we conduct extensive evaluations on both the NEU-DET and GC10-DET datasets. The robustness of GS-YOLO has also been verified under various simulated degradation conditions. Through extensive experiments and comparisons with other advanced models, GS-YOLO shows significant advantages, achieving an outstanding balance between model lightweighting and superior detection performance. Our code is available at: https://github.com/niuniuhhh/GS-YOLO/tree/main .

Multi-view unsupervised feature selection (MV-UFS) is a fundamental task in high-dimensional data analysis, aiming to identify discriminative features while preserving intrinsic data structures. Despite its importance, existing MV-UFS methods face several critical limitations. Primarily, the reliance on centralized data storage raises significant privacy concerns and violates strict regulatory constraints. Furthermore, the independent construction of view-specific similarity graphs often fails to capture nuanced cross-view correlations, while fixed similarity metrics lack the necessary adaptability to handle complex, heterogeneous data. To address these challenges, we propose a novel privacy-preserving federated MVUFS framework. This framework synergistically integrates decentralized learning with adaptive multiview optimization to overcome the aforementioned bottlenecks. Specifically, a federated optimization scheme enables collaborative model training without raw data transmission, ensuring rigorous privacy. Simultaneously, an adaptive consensus graph learning mechanism constructs a unified similarity matrix by dynamically fusing multiview structures to enhance noise robustness. Moreover, a joint sparse regression model optimizes feature weights and pseudo-labels to prune redundant features while maintaining global cluster structures. Extensive experiments on eighteen real-world datasets demonstrate superior performance while effectively mitigating privacy risks.

Multi-view clustering has been empirically shown to improve learning performance by leveraging the inherent complementary information across multiple views of data. However, in real-world scenarios, collecting strictly aligned views is challenging, and learning from both aligned and unaligned data becomes a more practical solution. Partially View-aligned Clustering (PVC) aims to learn correspondences between misaligned view samples to better exploit the potential consistency and complementarity across views, including both aligned and unaligned data. However, most existing PVC methods fail to leverage unaligned data to capture the shared semantics among samples from the same cluster. Moreover, the inherent heterogeneity of multi-view data induces distributional shifts in representations, leading to inaccuracies in establishing meaningful correspondences between cross-view latent features and, consequently, impairing learning effectiveness. To address these challenges, we propose a Semantic MAtching contRasTive learning model (SMART) for PVC. The main idea of our approach is to alleviate the influence of cross-view distributional shifts, thereby facilitating semantic matching contrastive learning to fully exploit semantic relationships in both aligned and unaligned data. Specifically, we mitigate view distribution shifts by aligning cross-view covariance matrices, which enables the inference of a semantic graph for all data. Guided by the learned semantic graph, we further exploit semantic consistency across views through semantic matching contrastive learning. After the optimization of the above mechanisms, our model smoothly performs semantic matching for different view embeddings instead of the cumbersome view realignment, which enables the learned representations to enjoy richer category-level semantics and stronger robustness. Extensive experiments on eight benchmark datasets demonstrate that our method consistently outperforms existing approaches on the PVC problem. The code is available at https://github.com/THPengL/SMART.

Multi-layer network community detection is a crucial topic in Industrial Internet of things(IIoT). Due to communication and privacy requirements, network data is distributed across multiple devices, being a significant challenge to develop a model to learn latent information for community detection. To address the problem, this paper proposes a federated fuzzy C-Means for multi-layer network community detection. Firstly, non-negative matrix factorization is employed to obtain a low-dimensional representation via training local data in each client. The gradients of the global centroids are then transmitted to a central server for consistent fusion and complete community detection within the fuzzy C-Means framework. As a result, the training process for each client remains independent and leads to effectively privacy preservation. Experimental results demonstrate that the proposed method can successfully perform multi-layer network community detection across distributed devices and achieve comparable performance in contrast with centralized community detection methods on four public datasets.

This article investigates the problem of bipartite synchronization with a non-autonomous leader in fractional-order multi-layer signed network. To address the chattering phenomenon induced by the use of sign functions in existing studies for mitigating the influence of bounded unknown leader inputs, a continuous controller and a dynamic event-triggered controller are proposed, respectively. The continuous controller is introduced a decay function to ensure the continuity of the control input, thereby effectively avoids chattering. The dynamic event-triggered controller leverages the characteristics of event-triggered control, the input remains constant within triggered intervals, which confines discontinuities to discrete triggering instants. This approach not only eliminates chattering but also reduces the frequency of control updates. Besides, the measurement error is designed by synchronization error and introduce a new trigger mechanism, avoiding the issue of the existence of the fractional-order right-Dini derivative at zero caused by measurement error designed through the controller with the sign function. Notably, the controllers proposed in this paper are not only applicable to non-autonomous systems but also to those subject to external disturbances as well as traditional autonomous leader systems. The stability of the error system is demonstrated by Lyapunov method. In the end, the proposed corollaries and theorems are verified by three simulations.

Cancer subtyping is crucial for categorizing patients into distinct groups, enabling precision medicine and personalized therapies. As multi-omic analysis becomes more prevalent, integrating data from various omics provides deeper insights into the potential relationships between cancer subtypes. Although most cancer subtyping methods show promising performance, they have several limitations. These methods fail to account for omic differences, address noise in similarity matrices, and preserve the manifold structure of high-dimensional data in lowdimensional space. This study proposes a Robust Diverse Multiview Learning (RDML) model for cancer subtyping. Specifically, multi-view self-representation matrices are formulated as a thirdorder tensor. Differences between views are captured using an orthogonal diversity term, thereby reducing the redundant information between views. To enhance robustness of model to noise, we explicitly separate the self-representation tensor into a clean tensor and a noise tensor. Additionally, Laplacian manifold regularization is employed to preserve the local structure of highdimensional data in low-dimensional space. An efficient algorithm is designed to solve the proposed model. Comprehensive experiments are conducted on ten datasets, demonstrating the superior performance of the proposed model.

Analyzing multimedia data, which often comprises diverse views such as text, images, and videos, presents unique challenges for data processing. Deep matrix factorization (DMF) provides an elegant way to obtain reduced-dimensional representation of the multiview data produced by multimedia. Compared with single-layer matrix factorization, DMF can better discover the hierarchical information in a layerwise technique. However, the existing multiview DMF methods still have several problems: 1) the standard DMF using Frobenius norm fails to process data containing noises and outliers; 2) most DMF methods neglect to exploit the feature diversity to learn a more discriminative representation; and 3) in graph learning methods for DMF, the$k$NN method is utilized to construct data graphs, which results in many incorrect neighbor assignments. To address these issues, a robust multiview deep nonnegative matrix factorization with feature diversity and optimal graph learning (RMvDNMF-FG) is proposed for clustering in this article. Specifically, a noise-insensitive logarithmic loss function is designed to measure the factorization error, inner products of basis vectors are minimized to achieve feature diversity for obtaining discriminative representation, and an optimal graph construction strategy is proposed to maintain the geometric structure of the data. To solve the proposed model, we explore an iterative updating algorithm that makes the objective function decrease consistently as the number of iterations increases. Additionally, the convergence proof of the iterative updating algorithm is provided with detailed mathematical analysis. Furthermore, through numerous comparative experiments with eleven state-of-the-art algorithms on five multiview datasets, the effectiveness of the proposed method is demonstrated.

Multilayer networks community detection plays an important role in data mining. It can discover the latent representations of network structures for effectively completing downstream tasks. However, existing community detection methods rarely consider the relationships between multilayer networks. In addition, the noise contained in the networks always leads to the degradation of detection performance. To address the above issues, this article proposes an orthogonal symmetric nonnegative matrix factorization (SNMF) with low-rank tensor representation (OSNMFTR) for multilayer networks community detection. Specifically, the proposed approach obtains the latent representation of each network via orthogonal SNMF, then a clean self-representation tensor is got based on subspace learning. Finally, to discover the high-order relationships among each network, a weighted tensor nuclear norm is utilized to constrain the tensor to make it low-rank. An algorithm based on the alternating direction method of multipliers (ADMMs) is designed to solve the OSNMFTR model. The experiments on nine datasets show the superior performance of the proposed approach.

Unbalanced incomplete multiview data are widely generated in engineering areas due to sensor failures, data acquisition limitations, etc. However, current research works are rarely focused on unbalanced incomplete multiview unsupervised feature selection (MUFS). To address this issue, this article proposes an MUFS method called unbalanced incomplete multiview unsupervised feature selection with low-redundancy constraint in low-dimensional space (UIMUFSLR). Specifically, the proposed method mitigates the impact of missing samples by learning a unified graph with assigning weights of samples adaptively. In addition, a novel regularization is designed by utilizing the inner product of selected features to obtain low redundancy. An iterative optimization algorithm is devised for UIMUFSLR, accompanied by a comprehensive analysis of its convergence behavior and computational complexity. Experimental results demonstrate the competitiveness of UIMUFSLR in handling unbalanced incomplete multiview data on seven public datasets.

This article investigates a class of systems of nonlinear equations (SNEs). Three distributed neurodynamic models (DNMs), namely a two-layer model (DNM-I) and two single-layer models (DNM-II and DNM-III), are proposed to search for such a system's exact solution or a solution in the sense of least-squares. Combining a dynamic positive definite matrix with the primal-dual method, DNM-I is designed and it is proved to be globally convergent. To obtain a concise model, based on the dynamic positive definite matrix, time-varying gain, and activation function, DNM-II is developed and it enjoys global convergence. To inherit DNM-II's concise structure and improved convergence, DNM-III is proposed with the aid of time-varying gain and activation function, and this model possesses global fixed-time consensus and convergence. For the smooth case, DNM-III's globally exponential convergence is demonstrated under the Polyak-Łojasiewicz (PL) condition. Moreover, for the nonsmooth case, DNM-III's globally finite-time convergence is proved under the Kurdyka-Łojasiewicz (KL) condition. Finally, the proposed DNMs are applied to tackle quadratic programming (QP), and some numerical examples are provided to illustrate the effectiveness and advantages of the proposed models.

Graph-based multiview clustering (MVC) approaches have demonstrated impressive performance by leveraging the consistency properties of multiview data in an unsupervised manner. However, existing methods for graph learning heavily rely on either Euclidean structures or the manifold topological structures derived from fixed view-specific graphs. Unfortunately, these approaches may not accurately reflect the consensus topological structure in a multiview setting. To address this limitation and enhance the intrinsic graph learning process, an adaptive exploration of a more appropriate consistency topological structure is required. Toward this end, we propose a novel approach called collaborative topological graph learning (CTGL) for MVC. The key idea is to adaptively discover the consistent topological structure to guide intrinsic graph learning. We achieve this by introducing an auxiliary consistency graph that formulates the topological relevance learning function. However, estimating the auxiliary consistency graph is not straightforward, as it is based on the learned view-specific graphs and requires prior availability. To overcome this challenge, we develop a collaborative learning strategy that simultaneously learns both the auxiliary consistency graph and view-specific graphs using tensor learning techniques. This strategy enables the adaptive exploration of the consistency topological structure during graph learning, resulting in more accurate clustering outcomes. Extensive experiments are provided to show the effectiveness of the proposed method. The source code can be found at https://github.com/CLiu272/CTGL.

Incomplete Multi-View Clustering (IMVC) is a promising topic in multimedia as it breaks the data completeness assumption. Most existing methods solve IMVC from the perspective of graph learning. In contrast, self-representation learning enjoys a superior ability to explore relationships among samples. However, only a few works have explored the potentiality of self-representation learning in IMVC. These self-representation methods infer missing entries from the perspective of whole samples, resulting in redundant information. In addition, designing an effective strategy to retain salient features while eliminating noise is rarely considered in IMVC. To tackle these issues, we propose a novel self-representation learning method with missing sample recovery and enhanced low-rank tensor regularization. Specifically, the missing samples are inferred by leveraging the local structure of each view, which is constructed from available samples at the feature level. Then an enhanced tensor norm, referred to as Logarithm-p norm is devised, which can obtain an accurate cross-view description by adaptive weights. Our proposed method achieves exact subspace representation in IMVC by leveraging high-order correlations and inferring missing information at the feature level. Extensive experiments on several widely used multi-view datasets demonstrate the effectiveness of the proposed method.

Multi-view clustering has garnered growing attention due to its ability to learn consistent representation across different views in order to enhance clustering performance. The majority of current research concentrates on aligning the feature distribution of the potential space to capture view-common information, disregarding the conflict between consistency alignment and the reconstruction objective. In this paper, we propose a multi-view clustering method via Separable Consistency and Diversity Feature Learning (SCDFL) to address the aforementioned issue. The proposed method decouples potential feature into two components for learning consistency and diversity, respectively, and integrates these features for data reconstruction. The consistency and diversity feature are concatenated for spectral clustering. Extensive experiments have demonstrated that our method achieves superior performance compared to several state-of-the-art methods.

Social media has a significant influence, which greatly facilitates people to stay up-to-date with information. Unfortunately, a great deal of fake news on social media misleads people and causes a lot of losses. Therefore, fake news detection is necessary to address this issue. Recently, social content category-based methods have become a crucial component of fake news detection. Different from news context-based category, which focuses on word embedding, it tends to explore the potential relationships and structures between users and news. In this article, a third-order tensor, which obtains massive information and connections, is constructed by the social links and engagements of social networks. Then, a sparse and graph-regularized CANDECOMP/PARAFAC (SGCP) tensor decomposition learning method is proposed for fake news detection on social network. In SGCP, a news factor matrix is constructed by CP decomposition of the tensor, which reflects the complex connections among users and news. Furthermore, SGCP retains sparsity of the news factor matrix and preserves the manifold structures from the original space. In addition, an efficient optimization algorithm, which is proven to be monotonically nonincreasing, is proposed to solve SGCP. Finally, abundant experiments are conducted on real-world datasets and demonstrate the effectiveness of the proposed SGCP.

Multiview clustering performs grouping a set of objects by utilizing complementary information from multiple views. Unfortunately, the clustering performance degenerates dramatically if the views are corrupted by noise. To overcome this limitation, we propose a robust multiview clustering approach based on weighted low-rank tensor approximation and noise separation. The proposed model improves the performance through a low-rank approximation function and weighted singular values. The weighted low-rank tensor approximation method considers both prior knowledge and the physical meanings associated with different singular values, leading to superior performance in capturing high-order correlations. Additionally, to eliminate mixed noise, a novellCauchy,1norm is developed to handle outliers, and thel1and Frobenius norms are used to handle random corruptions and slight perturbations, respectively. A high-efficiency optimization algorithm based on the alternating direction method of multipliers (ADMM) is designed to address the challenging proposed model. Experimental results on nine real-world datasets show that the proposed approach outperforms eight state-of-the-art multiview methods. Furthermore, experiments on various kinds of noise demonstrate the superior robustness of the proposed approach. Especially, in the mixed noise condition, the proposed approach is significantly superior to other methods.

Incomplete multi-view clustering (IMVC) presents a significant challenge due to the need for effectively exploring complementary and consistent information within the context of missing views. One promising strategy to tackle this challenge is to recover missing views by inferring the missing samples. However, such approaches often fail to fully utilize discriminative structural information or adequately address consistency, as it requires such information to be known or learnable in advance, which contradicts the incomplete data setting. In this study, we propose a novel approach calledLatentStructure-Aware view recovery (LaSA) for the IMVC task. Our objective is to recover missing views through discriminative latent representations by leveraging structural information. Specifically, our method offers a unified closed-form formulation that simultaneously performs missing data inference and latent representation learning, using a learned intrinsic graph as structural information. This formulation, incorporating graph structure information, enhances the inference of missing data while facilitating discriminative feature learning. Even when intrinsic graph is initially unknown due to incomplete data, our formulation allows for effective view recovery and intrinsic graph learning through an iterative optimization process. To further enhance performance, we introduce an iterative consistency diffusion process, which effectively leverages the consistency and complementary information across multiple views. Extensive experiments demonstrate the effectiveness of the proposed method compared to state-of-the-art approaches.

In this work, we study a more realistic challenging scenario in multiview clustering (MVC), referred to as incomplete MVC (IMVC) where some instances in certain views are missing. The key to IMVC is how to adequately exploit complementary and consistency information under the incompleteness of data. However, most existing methods address the incompleteness problem at the instance level and they require sufficient information to perform data recovery. In this work, we develop a new approach to facilitate IMVC based on the graph propagation perspective. Specifically, a partial graph is used to describe the similarity of samples for incomplete views, such that the issue of missing instances can be translated into the missing entries of the partial graph. In this way, a common graph can be adaptively learned to self-guide the propagation process by exploiting the consistency information, and the propagated graph of each view is in turn used to refine the common self-guided graph in an iterative manner. Thus, the associated missing entries can be inferred through graph propagation by exploiting the consistency information across all views. On the other hand, existing approaches focus on the consistency structure only, and the complementary information has not been sufficiently exploited due to the data incompleteness issue. By contrast, under the proposed graph propagation framework, an exclusive regularization term can be naturally adopted to exploit the complementary information in our method. Extensive experiments demonstrate the effectiveness of the proposed method in comparison with state-of-the-art methods. The source code of our method is available at the https://github.com/CLiu272/TNNLS-PGP.

In this article, sparse nonnegative matrix factorization (SNMF) is formulated as a mixed-integer bicriteria optimization problem for minimizing matrix factorization errors and maximizing factorized matrix sparsity based on an exact binary representation of$l_{0}$matrix norm. The binary constraints of the problem are then equivalently replaced with bilinear constraints to convert the problem to a biconvex problem. The reformulated biconvex problem is finally solved by using a two-timescale duplex neurodynamic approach consisting of two recurrent neural networks (RNNs) operating collaboratively at two timescales. A Gaussian score (GS) is defined as to integrate the bicriteria of factorization errors and sparsity of resulting matrices. The performance of the proposed neurodynamic approach is substantiated in terms of low factorization errors, high sparsity, and high GS on four benchmark datasets.

This article proposes a novel fixed-time converging proximal neurodynamic network (FXPNN) via a proximal operator to deal with equilibrium problems (EPs). A distinctive feature of the proposed FXPNN is its better transient performance in comparison to most existing proximal neurodynamic networks. It is shown that the FXPNN converges to the solution of the corresponding EP in fixed-time under some mild conditions. It is also shown that the settling time of the FXPNN is independent of initial conditions and the fixed-time interval can be prescribed, unlike existing results with asymptotical or exponential convergence. Moreover, the proposed FXPNN is applied to solve composition optimization problems (COPs),$l_{1}$-regularized least-squares problems, mixed variational inequalities (MVIs), and variational inequalities (VIs). It is further shown, in the case of solving COPs, that the fixed-time convergence can be established via the Polyak–Lojasiewicz condition, which is a relaxation of the more demanding convexity condition. Finally, numerical examples are presented to validate the effectiveness and advantages of the proposed neurodynamic network.

This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or inequality constraints. The proposed approach employs two recurrent neural networks operating concurrently at two timescales. In addition, particle swarm optimization is used to update the initial neuronal states iteratively to escape from local minima toward better initial states. In spite of its minimal system complexity, the approach is proven to be almost surely convergent to optimal solutions. Its superior performance is substantiated via solving five benchmark problems.

This paper addresses task assignment (TA) for multivehicle systems. Multivehicle TA problems are formulated as a combinatorial optimization problem and further as a global optimization problem. To fulfill heterogeneous tasks, cooperation among heterogeneous vehicles is incorporated in the problem formulations. A collaborative neurodynamic optimization approach is developed for solving the TA problems. Experimental results on four types of TA problems are discussed to substantiate the efficacy of the approach.

This paper presents a two-timescale duplex neurodynamic system for constrained biconvex optimization. The two-timescale duplex neurodynamic system consists of two recurrent neural networks (RNNs) operating collaboratively at two timescales. By operating on two timescales, RNNs are able to avoid instability. In addition, based on the convergent states of the two RNNs, particle swarm optimization is used to optimize initial states of the RNNs to avoid local minima. It is proven that the proposed system is globally convergent to the global optimum with probability one. The performance of the two-timescale duplex neurodynamic system is substantiated based on the benchmark problems. Furthermore, the proposed system is applied for L1-constrained nonnegative matrix factorization.