Late Fusion Multi-view Clustering via Global and Local Alignment Maximization

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   Multi-view Clustering via Late Fusion Alignment Maximization

   Siwei Wang, Xinwang Liu, En Zhu et al. · 2019 · International Joint Conference on Artificial Intelligence · cited 259 times · Not summarized yet

   Abstract

   Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in many applications, we observe that most of existing methods directly combine multiple views to learn an optimal similarity for clustering. These methods would cause intensive computational complexity and over-complicated optimization. In this paper, we theoretically uncover the connection between existing k-means clustering and the alignment between base partitions and consensus partition. Based on this observation, we propose a simple but effective multi-view algorithm termed {Multi-view Clustering via Late Fusion …

2. Very close Not relevant

   One-Stage Incomplete Multi-view Clustering via Late Fusion

   Yi Zhang, Xin-Wang Liu, Si-Wei Wang et al. · 2021 · ACM Multimedia · cited 56 times · Not summarized yet

   Abstract

   As a representative of multi-view clustering (MVC), late fusion MVC (LF-MVC) algorithm has attracted intensive attention due to its superior clustering accuracy and high computational efficiency. One common assumption adopted by existing LF-MVC algorithms is that all views of each sample are available. However, it is widely observed that there are incomplete views for partial samples in practice. In this paper, we propose One-Stage Late Fusion Incomplete Multi-view Clustering (OS-LF-IMVC) to address this issue. Specifically, we propose to unify the imputation of incomplete views and the clustering task into a single optimization procedure, so that the learning of the consensus partition matr…

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   Late Fusion Multiview Clustering via Min-Max Optimization

   Miao-Miao Li, Xin-Wang Liu, Yi Zhang et al. · 2023 · IEEE Transactions on Neural Networks and Learning Systems · cited 23 times · Not summarized yet

   Abstract

   Multiview clustering (MVC) sufficiently exploits the diverse and complementary information among different views to improve the clustering performance. As a representative algorithm of MVC, the newly proposed simple multiple kernel ${k}$ -means (SimpleMKKM) algorithm takes a min-max formulation and applies a gradient descent algorithm to decrease the resultant objective function. It is empirically observed that its superiority is attributed to the novel min-max formulation and the new optimization. In this article, we propose to integrate the min-max learning paradigm adopted by SimpleMKKM into late fusion MVC (LF-MVC). This leads to a tri-level max-min-max optimization problem with respect …

4. Very close Cited by this paper Used in depth by this paper A method it uses Not relevant

   Late Fusion Multiple Kernel Clustering With Local Kernel Alignment Maximization

   Tie-Jian Zhang, Xinwang Liu, Lei Gong et al. · 2023 · IEEE transactions on multimedia · cited 80 times · Not summarized yet

   Abstract

   Multi-view clustering, which appropriately integrates information from multiple sources to reveal data’s inherent structure, is gaining traction in clustering. Though existing procedures have yielded satisfactory results, we observe that they have neglected the inherent local structure in the base kernels. This may cause adverse effects on clustering. To solve the problem, we introduce LF-MKC-LKA, a simple yet effective late fusion multiple kernel clustering with local kernel alignment maximisation approach. In particular, we first determine the nearest $k$ neighbours in the average kernel space for each sample and record the information in the nearest neighbor indicator matrix. Then, the ne…

5. Very close Not relevant

   Trade-off Between Efficiency and Effectiveness: A Late Fusion Multi-view Clustering Algorithm

   Yunping Zhao, Wei-Xuan Liang, Jian-Zhuang Lu et al. · 2021 · Computers Materials & Continua · Not summarized yet

   Abstract

   Late fusion multi-view clustering (LFMVC) algorithms aim to integrate the base partition of each single view into a consensus partition. Base partitions can be obtained by performing kernel k-means clustering on all views. This type of method is not only computationally efficient, but also more accurate than multiple kernel k-means, and is thus widely used in the multi-view clustering context. LFMVC improves computational efficiency to the extent that the computational complexity of each iteration is reduced from O n ð Þ to O n ð Þ (where n is the number of samples). However, LFMVC also limits the search space of the optimal solution, meaning that the clustering results obtained are not idea…

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   Sharper Error Bounds in Late Fusion Multi-view Clustering with Eigenvalue Proportion Optimization

   Liang Du, Henghui Jiang, Xiaodong Li et al. · 2025 · AAAI Conference on Artificial Intelligence · cited 6 times · Not summarized yet

   Abstract

   Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance. Late Fusion Multi-View Clustering (LFMVC) has shown promise by synthesizing diverse clustering results into a unified consensus. However, current LFMVC methods struggle with noisy and redundant partitions and often fail to capture high-order correlations across views. To address these limitations, we present a novel theoretical framework for analyzing the generalization error bounds of multiple kernel k-means, leveraging local Rademacher complexity and principal eigenvalue proportions. Our analysis establishes a convergence rate of O(1/n), significantly improving upo…

7. Very close Not relevant

   One-Step Late Fusion Multi-View Clustering with Compressed Subspace

   Qiyuan Ou, Pei Zhang, Si-Hang Zhou et al. · 2024 · IEEE International Conference on Acoustics, Speech, and Signal Processing · cited 5 times · Not summarized yet

   Abstract

   Late fusion multi-view clustering (LFMVC) has become a rapidly growing class of methods in the multi-view clustering (MVC) field, owing to its excellent computational speed and clustering performance. One bottleneck faced by existing late fusion methods is that they are usually aligned to the average kernel function, which makes the clustering performance highly dependent on the quality of datasets. Another problem is that they require subsequent k-means clustering after obtaining the consensus partition matrix to get the final discrete labels, and the resulting separation of the label learning and cluster structure optimization processes limits the integrity of these models. To address the …

8. Very close Cited by this paper Used in depth by this paper Not relevant

   Late Fusion Incomplete Multi-view Clustering

   Xinwang Liu, Xinzhong Zhu, Miao-Miao Li et al. · 2018 · IEEE Transactions on Pattern Analysis and Machine Intelligence · cited 364 times · Not summarized yet

   Abstract

   Incomplete multi-view clustering optimally integrates a group of pre-specified incomplete views to improve clustering performance. Among various excellent solutions, multiple kernel $k$k-means with incomplete kernels forms a benchmark, which redefines the incomplete multi-view clustering as a joint optimization problem where the imputation and clustering are alternatively performed until convergence. However, the comparatively intensive computational and storage complexities preclude it from practical applications. To address these issues, we propose Late Fusion Incomplete Multi-view Clustering (LF-IMVC) which effectively and efficiently integrates the incomplete clustering matrices generate…

Prior work it builds on

1. Close A method it uses Not relevant

   Multi-View Clustering via Deep Matrix Factorization

   Han-Dong Zhao, Zheng-Ming Ding, Y. Fu · 2017 · AAAI Conference on Artificial Intelligence · cited 533 times · Not summarized yet

   Abstract

   Multi-View Clustering (MVC) has garnered more attention recently since many real-world data are comprised of different representations or views. The key is to explore complementary information to benefit the clustering problem. In this paper, we present a deep matrix factorization framework for MVC, where semi-nonnegative matrix factorization is adopted to learn the hierarchical semantics of multi-view data in a layer-wise fashion. To maximize the mutual information from each view, we enforce the non-negative representation of each view in the final layer to be the same. Furthermore, to respect the intrinsic geometric structure in each view data, graph regularizers are introduced to couple t…

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   Consensus One-Step Multi-View Subspace Clustering

   Pei Zhang, Xinwang Liu, Jian Xiong et al. · 2020 · IEEE Transactions on Knowledge and Data Engineering · cited 207 times · Not summarized yet

   Abstract

   Multi-view clustering has attracted increasing attention in multimedia, machine learning and data mining communities. As one kind of the essential multi-view clustering algorithm, multi-view subspace clustering (MVSC) becomes more and more popular due to its strong ability to reveal the intrinsic low dimensional clustering structure hidden across views. Despite superior clustering performance in various applications, we observe that existing MVSC methods directly fuse multi-view information in the similarity level by merging noisy affinity matrices; and isolate the processes of affinity learning, multi-view information fusion and clustering. Both factors may cause insufficient utilization of…

3. Close Not relevant

   Efficient and Effective Regularized Incomplete Multi-View Clustering

   Xinwang Liu, Miao-Miao Li, Chang Tang et al. · 2020 · IEEE Transactions on Pattern Analysis and Machine Intelligence · cited 272 times · Not summarized yet

   Abstract

   Incomplete multi-view clustering (IMVC) optimally combines multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel $k$k-means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines IMVC as a joint optimization problem where the clustering and kernel matrix imputation tasks are alternately performed until convergence. Though demonstrating promising performance in various applications, we observe that the manner of kernel matrix imputation in MKKM-IK would incur intensive computational and storage complexities, over-complicated optimization and limitedly improved clustering performance. In…

4. Close Used in depth by this paper A method it uses Not relevant

   Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition

   Rong-Kai Xia, Yan Pan, Lei Du et al. · 2014 · AAAI Conference on Artificial Intelligence · cited 759 times · Not summarized yet

   Abstract

   Multi-view clustering, which seeks a partition of the data inmultiple views that often provide complementary information to eachother, has received considerable attention in recent years. In reallife clustering problems, the data in each view may haveconsiderable noise. However, existing clustering methods blindlycombine the information from multi-view data with possiblyconsiderable noise, which often degrades their performance. In thispaper, we propose a novel Markov chain method for RobustMulti-view Spectral Clustering (RMSC). Our method has a flavor oflow-rank and sparse decomposition, where we firstly construct atransition probability matrix from each single view, and then usethese matri…

5. Close Not relevant

   Multiview Clustering: A Scalable and Parameter-Free Bipartite Graph Fusion Method

   Xue-Long Li, Han Zhang, Rong Wang et al. · 2020 · IEEE Transactions on Pattern Analysis and Machine Intelligence · cited 487 times · Not summarized yet

   Abstract

   Multiview clustering partitions data into different groups according to their heterogeneous features. Most existing methods degenerate the applicability of models due to their intractable hyper-parameters triggered by various regularization terms. Moreover, traditional spectral based methods always encounter the expensive time overheads and fail in exploring the explicit clusters from graphs. In this paper, we present a scalable and parameter-free graph fusion framework for multiview clustering, seeking for a joint graph compatible across multiple views in a self-supervised weighting manner. Our formulation coalesces multiple view-wise graphs straightforward and learns the weights as well as…

Papers that cite it

1. Very close Uses this paper in depth Not relevant

   Continual Multi-view Clustering

   Xin-Hang Wan, Ji-Yuan Liu, Wei-Xuan Liang et al. · 2022 · ACM Multimedia · cited 70 times · Not summarized yet

   Abstract

   With the increase of multimedia applications, data are often collected from multiple sensors or modalities, encouraging the rapid development of multi-view (also called multi modal) clustering technique. As a representative, late fusion multi-view clustering algorithm has attracted extensive attention due to its low computation complexity yet promising performance. However, most of them deal with the clustering problem in which all data views are available in advance, and overlook the scenarios where data observations of new views are accumulated over time. To solve this issue, we propose a continual approach on the basis of late fusion multi-view clustering framework. In specific, it only n…

2. Very close Not relevant

   Scalable Cross-View Sample Alignment for Multi-View Clustering with View Structure Similarity

   Jun Wang, Zhenglai Li, Chang Tang et al. · 2025 · Neural Information Processing Systems · Not summarized yet

   Abstract

   Most existing multi-view clustering methods aim to generate a consensus partition across all views, based on the assumption that all views share the same sample arrangement. However, in real-world scenarios, the collected data across different views is often unsynchronized, making it difficult to ensure consistent sample correspondence between views. To address this issue, we propose a scalable sample-alignment-based multi-view clustering method, referred to as SSA-MVC. Specifically, we first employ a cluster-label matching (CLM) algorithm to select the view whose clustering labels best match those of the others as the benchmark view. Then, for each of the remaining views, we construct repre…

3. Close Not relevant

   EVA-MVC: Equitable View-weight Allocation for Generic Multi-View Clustering

   Yuan Fang, Xiao-Feng Feng, Geping Yang et al. · 2025 · The Web Conference · cited 1 time · Not summarized yet

   Abstract

   Contemporary datasets sourced from the web often adopt a multi-view format, collecting data from diverse sources, domains, or modules. Existing methodologies employed to analyze such datasets frequently overlook or inaccurately allocate the view-weights, pivotal metrics reflecting each view's significance. This work introduces EVA-MVC, a simple yet effective algorithm designed for Equitable View-weight Allocation (EVA) seamlessly integrated with arbitrary Multi-view Clustering (MVC) methods. Within the EVA module, we establish theoretical connections between view supplementarity and Multi-view Subspace Learning (MSL), leading to the partition of views into View Communities (VCs) based on the…

4. Close Not relevant

   One-pass View-unaligned Clustering

   Wen-Hua Dong, Xiao-Jun Wu, Zhen-Hua Feng et al. · 2024 · IEEE transactions on multimedia · cited 9 times · Not summarized yet

   Abstract

   Given a set of multi-view instances, the prevailing assumption in most existing clustering approaches is that they are complete and exhibit cross-view alignment. However, this assumption is often unrealistic. In such scenarios, it could be satisfied at the cost of data pre-processing, but this would be complex and inconsistent with practical applications. Therefore, developing more effective solutions for the View-unaligned Problem (VuP) is highly desirable. Several pioneering works have tackled the partially VuP, yet handling fully VuP remains a challenge due to the reliance on partially pre-aligned instances. In this paper, we propose One-pass View-unaligned Clustering (OpVuC) that simulta…

5. Related Not relevant

   Multiple Kernel Clustering with Shifted Laplacian on Grassmann Manifold

   Xi Wu, Chuang Huang, Xin-Li Liu et al. · 2024 · ACM Multimedia · cited 5 times · Not summarized yet

   Abstract

   Multiple kernel clustering (MKC) has garnered considerable attention, as their efficacy in handling nonlinear data in high-dimensional space. However, current MKC methods have three primary issues: (1) Solely focuses on clustering information while neglecting energy information and potential noise interference within the kernel; (2) The inherent manifold structure in the high-dimensional space is complex, and they lack the insufficient exploration of topological structure; (3) Most encounter cubic computational complexity, posing a formidable resource consumption challenge. To tackle the above issues, we propose a novel MKC method with shifted Laplacian on Grassmann manifold (sLGm). Firstly,…