Sep 2026· Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence· pp. 5297-5305· 0 citations· 30 references
TL;DR
The Dual-tOpology learning with adapTive Anchors (DOTA) is proposed, which not only learns the sample-anchor relationship but also preserves the topology structure among anchors, significantly enhancing the discriminability of learned representation while preserving the underlying data manifold.
Abstract
As a prominent paradigm for large-scale unsupervised learning, anchor-based multi-view clustering aims to reveal the latent structures across heterogeneous data representations with high efficiency. Despite achieving some progress, existing methods typically suffer from the following two limitations. Firstly, they rely on pre-constructed anchors or rigid constraints (e.g., orthogonality), whereas the intrinsic topological correlations among anchors are completely discarded. Secondly, most existing methods either perform clustering directly on the bipartite graph or treat different views equally, thereby failing to capture the global structural information. To this end, the Dual-tOpology learning with adapTive Anchors (DOTA) is proposed, which not only learns the sample-anchor relationship (i.e., bipartite graph) but also preserves the topology structure among anchors, significantly enhancing the discriminability of learned representation while preserving the underlying data manifold. By integrating an adaptive view-weighting strategy to balance view contributions, DOTA derives discriminative global sample embeddings by propagating spectral information through the bipartite graph. Extensive experiments on benchmark datasets demonstrate the superiority of DOTA.
Constrained multi-view clustering aims to integrate external prior knowledge and complementary information from multiple views to enhance clustering performance. However, existing approaches typically employ Euclidean distance to learn view-specific and consensus embeddings, which often fail to capture the intrinsic geometric structure of high-dimensional data. Moreover, the dependence on limited external constraints or pre-defined anchors usually leads to suboptimal generalization and sensitivity to anchor quality. To address these limitations, we propose a novel deep constrained multi-view clustering framework, namely SeSCE. Specifically, we encode view embeddings into a spherical space, leveraging pairwise constraints to maximize intra-class compactness and inter-class separability. Crucially, a confidence-aware pseudo-constraint mining mechanism is designed to distill reliable pairwise constraints from high-confidence predictions iteratively. This effectively bridges the gap between unsupervised feature learning and discriminative clustering by progressively sharpening cluster boundaries. Finally, a globally aware attention mechanism is introduced to facilitate adaptive multi-view fusion. Extensive experiments demonstrate the superiority of our algorithm over state-of-the-art methods.
Jun Wang, Zhenglai Li, Chuan Tang et al.· IEEE Transactions on Pattern...· 0 citations
Multi-view Clustering via Manifold Decomposition is proposed, which directly infers cluster labels from multi-view data without explicit similarity graph construction or anchor selection, and reformulates multi-view clustering as a unified multi-view regression problem, where cluster labels are optimized as model variables.
Xiao-Wei Zhao, Xin-Yue Kou, Yan Chen et al.· Proceedings of the Thirty-Fi...· 0 citations
Multi-view subspace clustering has progressed significantly by using deep neural networks to handle nonlinear data representations. A recent advancement, the Multi-view Self-Expressive Subspace Clustering (MSESC) network, achieves markedly higher computational efficiency by substituting the traditional self-expression layer with a deep metric learning approach. Nevertheless, MSESC still suffers from two notable limitations: it fails to adequately capture the high-order geometric structures inherent in multi-view data, and it lacks effective guidance from the underlying clustering distribution. To overcome these shortcomings, we propose a novel framework termed Multi-View Graph Regularized Deep Metric Subspace Clustering (MVGR-DMSC). The proposed method introduces two key components into MSESC to enhance the discriminability of representations. First, a dual-order graph regularization module is devised to maintain both first-order and second-order manifold structures, thereby allowing the model to capture more complex local geometric relationships. Second, an adaptive view-weighted deep clustering module is incorporated, which employs the Kullback–Leibler divergence to guide representation learning while dynamically adjusting the contributions of different views. Through evaluations on five benchmark datasets, we show that MVGR-DMSC consistently yields better results than several state-of-the-art approaches, including the direct baseline MSESC, in both accuracy and robustness.
Peng-Peng Luo, Ming Yang, Chong Peng et al.· PLoS ONE· 0 citations
This paper proposes a feature-graph-guided adaptive Log-L2,1 sparse NMF with anchor dual graphs under a logarithmic framework that jointly integrate sample structure preservation, feature structure preservation, and feature-aware sparse learning within a unified graph-NMF model.
Quanrun Li, Tao Ma, Fangchen Xu et al.· Mathematics· 0 citations
Multi-view anchor graph clustering has emerged as a high-efficiency paradigm of multi-view learning. However, how to design an effective anchor alignment mechanism within this framework remains an open challenge, which is formally termed the Anchor-Unaligned Problem (AUP). Current research fails to adequately address two pivotal aspects of this challenge: first, the construction of anchor graphs and the alignment of anchors are two independent stages, overlooking their potential synergistic reinforcement; second, selecting anchors from different views as an alignment baseline often renders the clustering performance highly sensitive to the baseline choice. To address these issues, we propose a unified framework termed One-Step Self-Aligned Anchor Learning for Multi-View Clustering (OSAA-MVC). Departing from conventional two-stage strategies, we integrate anchor alignment and anchor graph construction into a joint optimization process, thereby enabling their mutual reinforcement to improve clustering performance. To avoid the baseline selection issue, we introduce a novel mixed anchor strategy, which effectively bypasses the necessity of manual baseline selection while simultaneously capturing both view-specific and cross-view information. This strategy can stabilize clustering performance at a consistently high level. Extensive experiments demonstrate the superior efficiency and effectiveness of our proposed method compared to state-of-the-art competitors. The code is available at https://github.com/Jiamiao2024/OSAA-MVC.
Zi-Jian Chen, Miao Jia, Xing-Chen Hu et al.· Proceedings of the Thirty-Fi...· 0 citations
Although existing bipartite graph-based multiview clustering (MVC) methods effectively exploit the structural relationships within multiview data, they exhibit three major limitations: 1) they primarily focus on direct similarities between data points and anchors, neglecting underlying neighborhood structures; 2) most existing methods fail to capture high-order correlations across bipartite graphs from different views; and 3) they overlook the relationships among anchor points, limiting the discriminative power of the learned graph. To address these challenges, we propose a unified framework, termed enhanced multiorder bipartite graph learning (EMOBGL) for MVC. The proposed EMOBGL method first constructs a second-order bipartite graph (SOBG) to capture both local and neighboring structural relationships between data points and anchors through first-order similarity (FOS) and second-order similarity (SOS). Then, the tensor Schatten- $p$ regularizer is incorporated to construct a multiorder bipartite graph (MOBG) to capture third-order similarity (TOS) across views. Meanwhile, the anchor structure regularization (ASR) is introduced to model anchor-anchor interactions, further enhancing the structural expressiveness and discriminability of the bipartite graph. The resulting EMOBGL model effectively integrates multiorder and multiview relationships within a unified framework, achieving robust and discriminative clustering performance. An efficient alternating direction method of multipliers (ADMMs) is developed to optimize the model, and we theoretically prove that the solution converges to a Karush-Kuhn-Tucker (KKT) stationary point. Extensive comparative experiments on 13 benchmark datasets demonstrate that the proposed EMOBGL consistently outperforms 13 state-of-the-art methods in both clustering accuracy and robustness. The source code is available at https://github.com/DongHuangTaiYi871/EMOBGL.
Yangjun Deng, Wenhao Deng, Longfei Ren et al.· IEEE Transactions on Neural...· 0 citations
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