Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences

Wang, Siwei; Liu, Xinwang; Liu, Suyuan; Jin, Jiaqi; Tu, Wenxuan; Zhu, Xiaoyan; Zhu, En

doi:10.48550/arxiv.2205.15075

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…The hyper-parameter settings are summarized in Table 1 of Appendix. The clustering performance is evaluated by four metrics, i.e., ACC, NMI, ARI, and F1, which are widely used in both deep clustering (Liu et al 2022c;Xia et al 2022c;Bo et al 2020;Tu et al 2020;Liu et al 2022f) and traditional clustering (Zhou et al 2019;Zhang et al 2022bZhang et al , 2021Liu et al 2022b;Chen et al 2022b,a;Zhang et al 2022aZhang et al , 2020Sun et al 2021;Wan et al 2022;Wang et al 2022bWang et al , 2021a.…”

Section: Methodsmentioning

confidence: 99%

Hard Sample Aware Network for Contrastive Deep Graph Clustering

Liu

Yang

Zhou

et al. 2023

AAAI

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Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample mining-based algorithms have achieved great attention for their promising performance. However, we find that the existing hard sample mining methods have two problems as follows. 1) In the hardness measurement, the important structural information is overlooked for similarity calculation, degrading the representativeness of the selected hard negative samples. 2) Previous works merely focus on the hard negative sample pairs while neglecting the hard positive sample pairs. Nevertheless, samples within the same cluster but with low similarity should also be carefully learned. To solve the problems, we propose a novel contrastive deep graph clustering method dubbed Hard Sample Aware Network (HSAN) by introducing a comprehensive similarity measure criterion and a general dynamic sample weighing strategy. Concretely, in our algorithm, the similarities between samples are calculated by considering both the attribute embeddings and the structure embeddings, better revealing sample relationships and assisting hardness measurement. Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones. In this way, our method can mine not only the hard negative samples but also the hard positive sample, thus improving the discriminative capability of the samples further. Extensive experiments and analyses demonstrate the superiority and effectiveness of our proposed method. The source code of HSAN is shared at https://github.com/yueliu1999/HSAN and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github.

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Section: Methodsmentioning

confidence: 99%

Hard Sample Aware Network for Contrastive Deep Graph Clustering

Liu

Yang

Zhou

et al. 2023

AAAI

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“…Graph structures, which can well describe the relationships of pairwise data, are widely adopted in the field of MVC [14]- [19]. In general, graph-based multi-view clustering achieves remarkable performance with two main procedures [20], [21], i.e., generating a graph for each view and operating graph fusion on the individual graph. For example, [22] generates the optimal consensus graph matrix from the linear combination of the base graph matrices from multiple views.…”

Section: Introductionmentioning

confidence: 99%

Continual Multi-view Clustering

Wan

Liu

Liang

et al. 2022

Proceedings of the 30th ACM International Conference on Multimedia

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Multi-view clustering (MVC) has gained broad attention owing to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging issue in MVC called the incomplete continual data problem (ICDP). In specific, most existing algorithms assume that views are available in advance and overlook the scenarios where data observations of views are accumulated over time. Due to privacy considerations or memory limitations, previous views cannot be stored in these situations. Some works are proposed to handle it, but all fail to address incomplete views. Such an incomplete continual data problem (ICDP) in MVC is tough to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views. We propose Fast Continual Multi-View Clustering with Incomplete Views (FCMVC-IV) to address it. Specifically, it maintains a consensus coefficient matrix and updates knowledge with the incoming incomplete view rather than storing and recomputing all the data matrices. Considering that the views are incomplete, the newly collected view might contain samples that have yet to appear; two indicator matrices and a rotation matrix are developed to match matrices with different dimensions. Besides, we design a three-step iterative algorithm to solve the resultant problem in linear complexity with proven convergence. Comprehensive experiments on various datasets show the superiority of FCMVC-IV.

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Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences

Cited by 2 publications

References 37 publications

Hard Sample Aware Network for Contrastive Deep Graph Clustering

Hard Sample Aware Network for Contrastive Deep Graph Clustering

Continual Multi-view Clustering

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