The use of anchors often leads to better efficiency and scalability, making them highly favored. However, there is a challenge in anchor-based multi-view subspace learning. A unified anchor graph overly emphasize the commonality between views, failing to adequately capture the view-specific individuality. This has led some models to independently explore the individuality of each view before aligning and integrating them, often achieving better performance but making the process more cumbersome. Therefore, this paper proposes a new model, simultaneously capturing the individuality and commonality between anchor graphs for multi-view clustering. The model has three notable advantages: First, it allows view-specific anchor graphs to align in real-time with a common anchor graph as a reference, eliminating the need for post-alignment. Second, it enforces a cluster-wise structure among anchors and balances sample distribution among them, providing strong discriminative power. Lastly, it maintains linear complexity with respect to the numbers of samples and anchors, avoiding the significant time costs associated with their increase. Comprehensive experiments demonstrate the effectiveness and efficiency of our method compared to various state-of-the-art algorithms.

Intrusion detection systems are a crucial technique for securing the Internet of Things (IoT) from malicious attacks. Additionally, due to the continuous emergence of new vulnerabilities and unknown attack types, only a small number of attack samples in the IoT environments can be captured for analysis. In this work, we introduce an anchor graph clustering (AGC) method for intrusion detection to address the challenge of limited labeled samples in the IoT environments. AGC initially transforms the raw data into the embedding space to obtain more representative anchors. Then, AGC unifies anchor graph construction, anchor graph learning, and graph clustering into a unified framework, solving the resulting optimization problem through an iterative solution algorithm. Finally, AGC leverages the powerful analytical capabilities of graph learning to achieve fine-grained classification of low-quality labels. Experimental results on both real and synthetic datasets confirm that AGC can identify intrusions with high precision, while also being time-efficient in detection.

As we all known, sparse subspace learning can provide good input for spectral clustering, thereby producing high-quality cluster partitioning. However, it employs complete samples as the dictionary for representation learning, resulting in non-negligible computational costs. Therefore, replacing the complete samples with representative ones (anchors) as the dictionary has become a more popular choice, giving rise to a series of related works. Unfortunately, although these works are linear with respect to the number of samples, they are often quadratic or even cubic with respect to the number of anchors. In this paper, we derive a simpler problem to replace the original scalable subspace clustering, whose properties are utilized. This new problem is linear with respect to both the number of samples and anchors, further enhancing scalability and providing more efficient operations. Furthermore, thanks to the new problem formulation, we can adopt a separate fusion strategy for multi-view extensions. This strategy can better measure the inter-view difference and avoid alternate optimization, so as to achieve more robust and efficient multi-view clustering. Finally, comprehensive experiments demonstrate that our methods not only significantly reduce time overhead but also exhibit superior performance.

In practical applications, multi-view subspace clustering is hindered by data noise that disrupts the ideal block-diagonal structure of self-representation matrices, thereby degrading performance. Moreover, many existing methods rely solely on sample features, overlooking the valuable structural information in affinity matrices (e.g., pairwise relationships). While conventional contrastive learning strategies often introduce false negative pairs due to noise and unreliable sample selection. To address these challenges, we propose a pseudo-label guided bidirectional discriminative deep multi-view subspace clustering method (PBDMSC). Our approach first employs pseudo-label guided contrastive learning, using previous cluster assignments to select reliable positive and negative samples, which mitigates incorrect pairings and enhances low-dimensional representations. Then, a discriminative self-representation learning method is introduced that leverages pseudo-labels to enforce homogeneous expression constraints and incorporates a bidirectional attention mechanism to preserve the structured information from affinity matrices, thereby enhancing robustness. Experimental results on six real-world datasets demonstrate that our proposed method achieves state-of-the-art clustering performance.

The goal of balanced clustering is partitioning data into distinct groups of equal size. Previous studies have attempted to address this problem by designing balanced regularizers or utilizing conventional clustering methods. However, these methods often rely solely on classic methods, which limits their performance and primarily focuses on low-dimensional data. Although neural networks exhibit effective performance on high-dimensional datasets, they struggle to effectively leverage prior knowledge for clustering with a balanced tendency. To overcome the above limitations, we propose deep semisupervised balanced clustering, which simultaneously learns clustering and generates balance-favorable representations. Our model is based on the autoencoder paradigm incorporating a semisupervised module. Specifically, we introduce a balance-oriented clustering loss and incorporate pairwise constraints into the penalty term as a pluggable module using the Lagrangian multiplier method. Theoretically, we ensure that the proposed model maintains a balanced orientation and provides a comprehensive optimization process. Empirically, we conducted extensive experiments on four datasets to demonstrate significant improvements in clustering performance and balanced measurements. Our code is available at https://github.com/DuannYu/BalancedSemi-TNNLS.

Graph anomaly detection (GAD) aims to identify anomalous graphs that significantly deviate from other ones, which has raised growing attention due to the broad existence and complexity of graph-structured data in many real-world scenarios. However, existing GAD methods usually execute with centralized training, which may lead to privacy leakage risk in some sensitive cases, thereby impeding collaboration among organizations seeking to collectively develop robust GAD models. Although federated learning offers a promising solution, the prevalent non-IID problems and high communication costs present significant challenges, particularly pronounced in collaborations with graph data distributed among different participants. To tackle these challenges, we propose an effective federated graph anomaly detection framework (FGAD). We first introduce an anomaly generator to perturb the normal graphs to be anomalous and train a powerful anomaly detector by distinguishing generated anomalous graphs from normal ones. We subsequently leverage a student model to distill knowledge from the trained anomaly detector (teacher model), which aims to maintain the personality of local models and alleviate the adverse impact of non-IID problems. Additionally, we design an effective collaborative learning mechanism that facilitates the personalization preservation of local models and significantly reduces communication costs among clients. Empirical results of diverse GAD tasks demonstrate the superiority and efficiency of FGAD.

Ensemble clustering has emerged as a powerful framework for analyzing heterogeneous and complex data. Despite the abundance of existing schemes, co-association matrix-based methods remain the mainstream approach. However, focusing solely on pairwise correlations falls short of fully capturing the intricate cluster relationships. Moreover, despite its potential, ensemble clustering has yet to effectively leverage the powerful representation capabilities of neural networks. To address these limitations, we propose a deep ensemble clustering method called Ensemble Clustering with Attentional Representation (ECAR). Our method considers the results of base partition as groups with related information to explore higher-order fusion information. ECAR captures the importance of each sample's association with its related group by employing an attentional network, and encodes this information into a low-dimensional representation. The attentional network is trained by jointly optimizing the clustering loss from soft assignments learned from the embeddings and the reconstruction loss from the weighted graph generated from ensemble clustering. During training, the weights of base partitions are adaptively refined to promote diversity and consistency while reducing the impact of low-quality and redundant base partitions. Extensive experimental results on real-world datasets demonstrate the substantial improvement of our method over existing baseline ensemble clustering methods and deep clustering methods.

Most of the existing multi-view clustering algorithms are performed in the original feature space, and their performance in heavily reliant on the quality of the raw data. Besides, some two-stage strategies cannot achieve ideal results due to the absence of capturing the correlation between views. In view of this, we propose Multi-View K-means with Laplacian Embedding (MVKLE), which is capable of clustering multi-view data in the learned embedding space. Specifically, we employ local structure-preserving dimensionality reduction to obtain the underlying representation of each view, and obtain the clustering results directly through an effective optimization formulation. Experiments on several common multi-view datasets demonstrate the superiority of the proposed method.

Multi-view clustering aims to discover common patterns from multi-source data, whose generality is remarkable. Compared with traditional methods, deep learning methods are data-driven and have a larger search space for solutions, which may find a better solution to the problem. In addition, more considerations can be introduced by loss functions, so deep models are highly reusable. However, compared with deep learning methods, traditional methods have better interpretability, whose optimization is relatively stable. In this paper, we propose a multi-view spectral clustering model, combining the advantages of traditional methods and deep learning methods. Specifically, we start with the objective function of traditional spectral clustering, perform multi-view extension, and then obtain the traditional optimization process. By partially parameterizing this process, we further design corresponding differentiable modules, and finally construct a complete network structure. The model is interpretable and extensible to a certain extent. Experiments show that the model performs better than other multi-view clustering algorithms, and its semi-supervised classification extension also has excellent performance compared to other algorithms. Further experiments also show the stability and fewer iterations of the model training.