Multi-View Graph Learning by Joint Modeling of Consistency and Inconsistency

Liang, Youwei; Huang, Dong; Wang, Chang‐Dong; Yu, Philip S.

doi:10.1109/tnnls.2022.3192445

Cited by 34 publications

(13 citation statements)

References 57 publications

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“…Xie et al [25] extended the tensorized multi-view subspace clustering by further incorporating a local structure constraint. Liang et al [5], [11] performed graph fusion on multiple K-NN graphs from multiple views with cross-view consistency and inconsistency jointly modeled.…”

Section: Multi-view Clusteringmentioning

confidence: 99%

“…The affinity graph construction is a basic step in many MVC algorithms, which formulates the sample-wise relationship by computing an N × N affinity matrix and generally takes O(N 2 d) time and O(N 2 ) space, where N is the number of samples and d is the dimension. The graph partitioning (typically by spectral clustering) is another computationally expensive step in many MVC algorithms [3], [4], [5], [6], [7], [8], which often requires singular value decomposition (SVD) and takes O(N 3 ) time and O(N 2 ) space. Especially, the graph partitioning via spectral clustering is adopted as an important step in many MVC algorithms, such as multiview spectral clustering [3], [8], multi-view subspace clustering [6], [9], and multi-view graph learning [4], [5], which, together with some other expensive matrix computations, contributes to the O(N 3 ) complexity bottleneck in these MVC algorithms [3], [4], [5], [6], [7], [8], [10].…”

Section: Introductionmentioning

confidence: 99%

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Fast Multi-View Clustering Via Ensembles: Towards Scalability, Superiority, and Simplicity

Huang

Wang

Lai

2023

IEEE Trans. Knowl. Data Eng.

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Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restricting their feasibility for large-scale datasets. Second, they typically fuse multi-view information via one-stage fusion, neglecting the possibilities in multi-stage fusions. Third, dataset-specific hyperparameter-tuning is frequently required, further undermining their practicability. In light of this, we propose a fast multi-view clustering via ensembles (FastMICE) approach. Particularly, the concept of random view groups is presented to capture the versatile view-wise relationships, through which the hybrid early-late fusion strategy is designed to enable efficient multi-stage fusions. With multiple views extended to many view groups, three levels of diversity (w.r.t. features, anchors, and neighbors, respectively) are jointly leveraged for constructing the view-sharing bipartite graphs in the early-stage fusion. Then, a set of diversified base clusterings for different view groups are obtained via fast graph partitioning, which are further formulated into a unified bipartite graph for final clustering in the late-stage fusion. Notably, FastMICE has almost linear time and space complexity, and is free of dataset-specific tuning. Experiments on 22 multi-view datasets demonstrate its advantages in scalability (for extremely large datasets), superiority (in clustering performance), and simplicity (to be applied) over the state-of-the-art. Code available: https://github.com/huangdonghere/FastMICE.

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Section: Multi-view Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast Multi-View Clustering Via Ensembles: Towards Scalability, Superiority, and Simplicity

Huang

Wang

Lai

2023

IEEE Trans. Knowl. Data Eng.

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“…CIFAR-100 ImageNet-10 ImageNet-Dogs Tiny-ImageNet K-means [17] 8.4 11.9 5.5 6.5 SC [18] 9.0 15.1 3.8 6.3 AC [43] 9.8 13.8 3.7 6.9 NMF [44] 7.9 (NMI) [40], clustering accuracy (ACC) [41], and adjusted rand index (ARI) [42].…”

Section: Datasetmentioning

confidence: 99%

Heterogeneous Tri-stream Clustering Network

Deng

Huang

Wang

2023

Neural Process Lett

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Contrastive deep clustering has recently gained significant attention with its ability of joint contrastive learning and clustering via deep neural networks. Despite the rapid progress, previous works mostly require both positive and negative sample pairs for contrastive clustering, which rely on a relative large batch-size. Moreover, they typically adopt a two-stream architecture with two augmented views, which overlook the possibility and potential benefits of multi-stream architectures (especially with heterogeneous or hybrid networks). In light of this, this paper presents a new end-to-end deep clustering approach termed Heterogeneous Tri-stream Clustering Network (HTCN). The tri-stream architecture in HTCN consists of three main components, including two weight-sharing online networks and a target network, where the parameters of the target network are the exponential moving average of that of the online networks. Notably, the two online networks are trained by simultaneously (i) predicting the instance representations of the target network and (ii) enforcing the consistency between the cluster representations of the target network and that of the two online networks. Experimental results on four challenging image datasets demonstrate the superiority of HTCN over the state-of-the-art deep clustering approaches. The code is available at https://github.com/dengxiaozhi/HTCN.

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“…Recently, the consistency of graph-based multi-view clustering has attracted considerable attention. For example, Reference [13] proposed a method to simultaneously construct the consistency and inconsistency of multiple views in the objective function to delete the inconsistent parts and preserve the consistent parts. However, Reference [14] mainly focuses on the consistency and inconsistency of multi-view subspace clustering.…”

Section: Introductionmentioning

confidence: 99%

Self-Weighted Graph-Based Framework for Multi-View Clustering

Yusof

2023

IEEE Access

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Multiple perspectives can be used to explore rich and complex datasets that are widely used in many applications. However, in real-world applications, the multi-view data are often noisy because of various environmental factors. The key challenge of graph-based multi-view clustering is obtaining a consistent clustering structure. Most graph-based methods learn independently in one view how similar the data points in each view are to one another. However, the consistency of information in a multi-view dataset is easily overlooked, causing an unsatisfactory unified graph matrix. To address this problem, this study proposes a novel self-weighted graph-based framework for multi-view clustering (SGMF). This framework integrates these initial similar graph matrices into a unified graph matrix by assigning different weights to the initial graphs of each view. This algorithm's goal is to effectively learn a unified graph matrix and eliminate noise or irrelevant information from the dataset. This is the first time a generalized multi-view framework based on the L1-norm has been proposed. L1-norm can be used to improve the robustness of the algorithm. Particularly, SGMF can automatically weigh each view's initial similar graph matrix to solve the noise problem and obtain a consistent data structure. Simultaneously, a rank constraint is imposed on the graph Laplacian matrix of the final unified matrix, which helps to automatically divide the data points into the appropriate number of clusters. The experimental results on synthetic and real datasets verify the effectiveness and superiority of the proposed method over the latest baseline.

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Multi-View Graph Learning by Joint Modeling of Consistency and Inconsistency

Cited by 34 publications

References 57 publications

Fast Multi-View Clustering Via Ensembles: Towards Scalability, Superiority, and Simplicity

Fast Multi-View Clustering Via Ensembles: Towards Scalability, Superiority, and Simplicity

Heterogeneous Tri-stream Clustering Network

Self-Weighted Graph-Based Framework for Multi-View Clustering

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