Graph-based clustering algorithms aim to construct an affinity graph that accurately captures the intrinsic structure of a dataset. To achieve this goal, these algorithms often use the k-nearest-neighbor (k-nn) method to build a graph regularizer for the required affinity graph, enabling it to have a grouping effect. However, due to the complex nature of real-world data, the k-nn method often fails to capture the true neighborhood relationships of a dataset, which in turn limits the quality of the learned affinity graph. Motivated by the insight that a learned affinity graph itself can more effectively reflect the underlying data structure, we propose a new graph-based clustering method, termed Self-learned Graph Regression (SGR). Unlike traditional approaches, SGR constructs its graph regularizer directly from the affinity graph being learned, allowing the graph to adaptively capture more accurate structural information. To solve the proposed problem, we develop an optimization algorithm along with an acceleration strategy. We further analyze the convergence and computational complexity of the proposed algorithm. Extensive clustering experiments on various benchmark datasets demonstrate that our method outperforms the state-of-the-art graph-based clustering algorithms.

Accurate traffic flow prediction is crucial to alleviating traffic congestion, optimizing road resource allocation, and enhancing public travel experience. However, the complex and dynamic nature of spatial-temporal heterogeneity in traffic flow poses challenges to this task. Existing methods often neglect the interactions between spatial-temporal correlations and struggle to capture dynamic traffic patterns across various perspectives. To address these issues, we propose a Spatial-Temporal Multi-Scale Interactive Graph Convolutional Network (STMIGCN) for traffic flow prediction. In this work, we employ a downsampling-based interactive learning framework to model traffic spatial-temporal patterns from a global-to-local perspective. This framework partitions and extracts spatial-temporal features at different scales, employing an interactive information passing and feedback mechanism to deeply mine latent spatial-temporal correlations. To address the static limitations of adaptive graphs, STMIGCN introduces a multi-graph fusion convolution method that incorporates dynamic features in spatial modeling, thereby enhancing the capture of dynamic spatial relationships between nodes. We perform comprehensive experiments on two real-world datasets and evaluate STMIGCN’s performance against ten baseline models. The results indicate that STMIGCN demonstrates more advanced performance.

Abstract Vessel re-identification (re-ID) is a crucial task in maritime supervision, enhancing maritime safety and improving the maritime situational awareness system. However, distinct from land-based scenarios involving vehicles or pedestrians, vessels, as enormous rigid bodies situated in the dynamic marine environment, face unique challenges such as significant variations in the scale of discriminative features and unpredictable sway. Furthermore, there is a limited number of publicly available datasets for vessel re-ID in complex backgrounds. In this paper, to overcome these challenges, a novel Hierarchical Perceptual Aggregation Network with Inclination-Aware Attention (HPAN-IAA) is proposed. HPAN-IAA comprises two main modules: the Hierarchical Perceptual Aggregation Block (HPAB) and the Inclination-Aware Attention Block (IAAB). Specifically, in HPAB, a hierarchical perceptual function is introduced to decompose visual information of vessels into discriminative features at multiple levels. These feature maps with different levels of detail from diverse network layers are then fused together by concatenation, resulting in a comprehensive feature representation that effectively integrates information across various scales. Conversely, to address the irregular variations and random omissions in discriminative feature distribution caused by unpredictable vessel sway, in IAAB, the Channel Collaborative Attention Module and the Pyramidal Spatial Attention Module are designed to adaptively extract potential discriminative features within each channel and spatial dimension, enhancing model’s ability in effectively extracting and utilizing irregularly changing discriminative features. Moreover, we propose a novel vessel re-ID dataset—VesselReID-2258. Extensive experiments conducted on VesselReID-2258 and the publicly available dataset VesselReID demonstrate that HPAN-IAA outperforms the current state-of-the-art methods,achieving superior performance with mean Average Precision scores of 0.861 and 0.823.

The key to address long-term person reidentification (Re-ID) in videos is to extract invariant spatio-temporal features (ISTF), which can be broadly categorized into two forms: 1) clothes-irrelevant appearance such as facial characteristics and body shape; 2) identity-distinctive motion such as posture and gait. However, existing studies mainly focus on mining either appearance- or motion-based features in the sequences without sufficient utilization of the ISTF. In this article, we propose an Appearance and gait-based tango network (AGT-Net) to comprehensively mine ISTF from appearance details and gait motions. Specifically, on the appearance detail branch, we introduce an invariance-aware video Swin transformer (IA-VST) to extract clothes-irrelevant appearance from the original RGB sequences. On the gait motion branch, we propose a motion-sensitive gait feature extractor (MS-GFE) to learn identity-distinctive motion from the pre-processed gait sequences. In addition, a score-level fusion strategy is introduced to integrate information from the two streams for prediction. Besides, since there is a lack of publicly available datasets, we propose a style-transferring synthetic long-term Video Re-ID (STYLE-VID) dataset, particularly for long-term Re-ID. Extensive experiments demonstrate that AGT-Net not only outperforms the state-of-the-art methods by up to 1.5% mAP on STYLE-VID, but also achieves comparable performance to other models on the traditional short-term Re-ID dataset MARS.

Multiview subspace clustering (MVSC) aims to integrate complementary information from different views to accurately reveal the subspace structure of a multiview dataset. Traditional MVSC methods often emphasize the aggregation of samples within the same subspace, while neglecting the separation of samples across different subspaces. In this article, we incorporate contrastive learning techniques into the MVSC framework, developing a contrastive data self-representation module, a contrastive regularizer for the reconstruction coefficient matrix in each view, and a contrastive alignment term to obtain a consensus coefficient matrix that fuses structural information from the reconstruction coefficient matrices. This leads to the framework of a purely contrastive MVSC (PCMVSC) approach. We elaborate on the superiority of the proposed modules in PCMVSC over similar ones in existing methods and show that the consensus reconstruction coefficient matrix obtained by PCMVSC can effectively uncover the underlying subspace structure of multiview datasets. Extensive subspace clustering experiments prove the effectiveness of PCMVSC and reveal that it outperforms various existing multiview clustering algorithms.

Modern vessels integrate a massive digital infrastructure and navigation-dependent operating systems, allowing for ship-to-shore and ship-to-ship collaborative communication. However, the heightened interconnection of various maritime infrastructures inevitably amplifies the risk of vessel navigation and communication. Existing intrusion detection techniques were usually built on individual network events, failing to account for the multi-event long-term dependency problem caused by the high latency and low bandwidth of ship communication networks, therefore cannot tackle sophisticated cyber-ship attacks, resulting in lower accuracy in intrusion detection. In this paper, we propose a dependable Intrusion Detection System(IDS) based on Segment-wise CoTransformer(SegCoT) to detect cyber-ship intrusion events, which primarily contains a two-stage Network Pattern Extraction Component (NPEC) and an Intrusion Event Identification Component (IEIC). The NPEC automates the extraction of long-term dependency of massive intrusion events employing a SegEvent-wise Attention (SEA). Furthermore, the extracted dependencies are leveraged by the IEIC for specific intrusion type detection from a spatio-temporal feature fusion perspective. Based on a cyber-ship dataset collected from real ocean-going vessels, the proposed model achieves 99% intrusion detection accuracy, outperforming the existing state-of-the-art approaches.

Multi-view K-means clustering successfully generalizes K-means from single-view to multi-view, and obtains excellent clustering performance. In every view, it makes each data point close to the center of the corresponding cluster. However, multi-view K-means only considers the compactness of each cluster, but ignores the separability of different clusters, which is of great importance to producing a good clustering result. In this paper, we propose Discriminatively Fuzzy Multi-view K-means clustering with Local Structure Preserving (DFMKLS). On the basis of minimizing the distance between each data point and the center of the corresponding cluster, DFMKLS separates clusters by maximizing the distance between the centers of pairwise clusters. DFMKLS also relaxes its objective by introducing the idea of fuzzy clustering, which calculates the probability that a data point belongs to each cluster. Considering multi-view K-means mainly focuses on the global information of the data, to efficiently use the local information, we integrate the local structure preserving into the framework of DFMKLS. The effectiveness of DFMKLS is evaluated on benchmark multi-view datasets. It obtains superior performances than state-of-the-art multi-view clustering methods, including multi-view K-means.

Multiview subspace clustering (MVSC) has attracted an increasing amount of attention in recent years. Most existing MVSC methods first collect complementary information from different views and consequently derive a consensus reconstruction coefficient matrix to indicate the subspace structure of a multi-view data set. In this paper, we initially assume the existence of a consensus reconstruction coefficient matrix and then use it to build a consensus graph filter. In each view, the filter is employed for smoothing the data and designing a regularizer for the reconstruction coefficient matrix. Finally, the obtained reconstruction coefficient matrices from different views are used to create constraints for the consensus reconstruction coefficient matrix. Therefore, in the proposed method, the consensus reconstruction coefficient matrix, the consensus graph filter, and the reconstruction coefficient matrices from different views are interdependent. We provide an optimization algorithm to obtain their optimal values. Extensive experiments on diverse multi-view data sets demonstrate that our approach outperforms some state-of-the-art methods.

Subspace clustering is a powerful tool for grouping data samples into their underlying subspaces. In this paper, we propose an advanced subspace clustering algorithm called SCHAGF (Subspace Clustering with A Hybrid Adaptive Graph Filter). SCHAGF leverages the obtained reconstruction coefficient matrix to design a low-pass graph filter and a high-pass graph filter simultaneously. These graph filters are then integrated into a hybrid graph filter, which is used for designing a feature extraction function and a constraint for the reconstruction coefficient matrix. Then the hybrid graph filter and the coefficient matrix are iteratively updated to achieve optimal values. Our results demonstrate that the features extracted using the hybrid graph filter exhibit compactness within classes and discrimination between classes. Additionally, the new constraints significantly enhance the block-diagonal structure of the reconstruction coefficient matrix. Finally, plenty of subspace clustering experiments show that the SCHAGF outperforms the related algorithms. Moreover, by incorporating the thresholding technique, thresholding SCHAGF (TSCHAGF) is found to surpass some deep models.

The critical point for the success of spectral-type subspace clustering algorithms is to seek reconstruction coefficient matrices that can faithfully reveal the subspace structures of data sets. An ideal reconstruction coefficient matrix should have two properties: 1) it is block-diagonal with each block indicating a subspace; 2) each block is fully connected. We find that a normalized membership matrix naturally satisfies the above two conditions. Therefore, in this paper, we devise an idempotent representation (IDR) algorithm to pursue reconstruction coefficient matrices approximating normalized membership matrices. IDR designs a new idempotent constraint. And by combining the doubly stochastic constraints, the coefficient matrices which are close to normalized membership matrices could be directly achieved. We present an optimization algorithm for solving IDR problem and analyze its computation burden as well as convergence. The comparisons between IDR and related algorithms show the superiority of IDR. Plentiful experiments conducted on both synthetic and real-world datasets prove that IDR is an effective subspace clustering algorithm.

Spectral-type subspace clustering algorithms have shown excellent performance in many subspace clustering applications. The existing spectral-type subspace clustering algorithms either focus on designing constraints for the reconstruction coefficient matrix or feature extraction methods for finding latent features of original data samples. In this paper, inspired by graph convolutional networks, we use the graph convolution technique to develop a feature extraction method and a coefficient matrix constraint simultaneously. And the graph-convolutional operator is updated iteratively and adaptively in our proposed algorithm. Hence, we call the proposed method adaptive graph convolutional subspace clustering (AGCSC). We claim that, by using AGCSC, the aggregated feature representation of original data samples is suitable for subspace clustering, and the coefficient matrix could reveal the subspace structure of the original data set more faithfully. Finally, plenty of subspace clustering experiments prove our conclusions and show that AGCSC11We present the codes of AGCSC and the evaluated algorithms on https://github.com/weilyshmtu/AGCSC. outperforms some related methods as well as some deep models.

Due to the corruptions or noises that existed in real-world data sets, the affinity graphs constructed by the classical spectral clustering-based subspace clustering algorithms may not be able to reveal the intrinsic subspace structures of data sets faithfully. In this article, we reconsidered the data reconstruction problem in spectral clustering-based algorithms and proposed the idea of "relation reconstruction." We pointed out that a data sample could be represented by the neighborhood relation computed between its neighbors and itself. The neighborhood relation could indicate the true membership of its corresponding original data sample to the subspaces of a data set. We also claimed that a data sample's neighborhood relation could be reconstructed by the neighborhood relations of other data samples; then, we suggested a much different way to define affinity graphs consequently. Based on these propositions, a sparse relation representation (SRR) method was proposed for solving subspace clustering problems. Moreover, by introducing the local structure information of original data sets into SRR, an extension of SRR, namely structured sparse relation representation (SSRR) was presented. We gave an optimization algorithm for solving SRR and SSRR problems and analyzed its computation burden and convergence. Finally, plentiful experiments conducted on different types of databases showed the superiorities of SRR and SSRR.

In the original belief function (BF) theory, a precise-valued belief structure has been widely used to represent uncertain information. However, this mentioned belief structure is difficult to effectively measure the specific hesitant situation, especially when decision makers have a set of possible values for the belief assignments of focal elements. In order to model the hesitant nature of the behavior of people to make a decision under uncertainty, we propose a hesitant fuzzy belief structure (HFBS) that is based on the BF theory and the recent hesitant fuzzy set theory. We also present the novel rule of combination of HFBS that is used and evaluated in a wearable human activity recognition (HAR) system coupled with an extreme learning machine. The evaluation of this new HFBS approach is done from two benchmark datasets. We clearly show its effectiveness and its superiority compared to various methods used classically for the wearable HAR.