Effective and Efficient Graph Learning for Multi-view Clustering

• URL: http://arxiv.org/abs/2108.06734v1
• Date: Sun, 15 Aug 2021 13:14:28 GMT
• Title: Effective and Efficient Graph Learning for Multi-view Clustering
• Authors: Quanxue Gao, Wei Xia, Xinbo Gao, Dacheng Tao
• Abstract summary: We propose an effective and efficient graph learning model for multi-view clustering. Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
• Score: 173.8313827799077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the impressive clustering performance and efficiency in characterizing both the relationship between data and cluster structure, existing graph-based multi-view clustering methods still have the following drawbacks. They suffer from the expensive time burden due to both the construction of graphs and eigen-decomposition of Laplacian matrix, and fail to explore the cluster structure of large-scale data. Moreover, they require a post-processing to get the final clustering, resulting in suboptimal performance. Furthermore, rank of the learned view-consensus graph cannot approximate the target rank. In this paper, drawing the inspiration from the bipartite graph, we propose an effective and efficient graph learning model for multi-view clustering. Specifically, our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm, which well characterizes both the spatial structure and complementary information embedded in graphs of different views. We learn view-consensus graph with adaptively weighted strategy and connectivity constraint such that the connected components indicates clusters directly. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size. Extensive experimental results indicate that our method is superior to state-of-the-art methods.

Related papers

• Deep Contrastive Graph Learning with Clustering-Oriented Guidance [61.103996105756394]
  Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. Models estimate an initial graph beforehand to apply GCN. Deep Contrastive Graph Learning (DCGL) model is proposed for general data clustering.
  arXiv  Detail & Related papers  (2024-02-25T07:03:37Z)
• Homophily-enhanced Structure Learning for Graph Clustering [19.586401211161846]
  Graph structure learning allows refining the input graph by adding missing links and removing spurious connections. Previous endeavors in graph structure learning have predominantly centered around supervised settings. We propose a novel method called textbfhomophily-enhanced structure textbflearning for graph clustering (HoLe)
  arXiv  Detail & Related papers  (2023-08-10T02:53:30Z)
• GraphCoCo: Graph Complementary Contrastive Learning [65.89743197355722]
  Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. This paper proposes an effective graph complementary contrastive learning approach named GraphCoCo to tackle the above issue.
  arXiv  Detail & Related papers  (2022-03-24T02:58:36Z)
• ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering [52.491074276133325]
  We propose an augmentation-free graph contrastive learning framework to solve the problem of partial multi-view clustering. The proposed approach elevates instance-level contrastive learning and missing data inference to the cluster-level, effectively mitigating the impact of individual missing data on clustering.
  arXiv  Detail & Related papers  (2022-03-01T02:32:25Z)
• Fine-grained Graph Learning for Multi-view Subspace Clustering [2.4094285826152593]
  We propose a fine-grained graph learning framework for multi-view subspace clustering (FGL-MSC) The main challenge is how to optimize the fine-grained fusion weights while generating the learned graph that fits the clustering task. Experiments on eight real-world datasets show that the proposed framework has comparable performance to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-01-12T18:00:29Z)
• Multiple Graph Learning for Scalable Multi-view Clustering [26.846642220480863]
  We propose an efficient multiple graph learning model via a small number of anchor points and tensor Schatten p-norm minimization. Specifically, we construct a hidden and tractable large graph by anchor graph for each view. We develop an efficient algorithm, which scales linearly with the data size, to solve our proposed model.
  arXiv  Detail & Related papers  (2021-06-29T13:10:56Z)
• Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view [28.779909990410978]
  Graph-based subspace clustering methods have exhibited promising performance. They still suffer some of these drawbacks: encounter the expensive time overhead, fail in exploring the explicit clusters, and cannot generalize to unseen data points. We propose a scalable graph learning framework, seeking to address the above three challenges simultaneously.
  arXiv  Detail & Related papers  (2021-02-16T03:46:11Z)
• Structured Graph Learning for Clustering and Semi-supervised Classification [74.35376212789132]
  We propose a graph learning framework to preserve both the local and global structure of data. Our method uses the self-expressiveness of samples to capture the global structure and adaptive neighbor approach to respect the local structure. Our model is equivalent to a combination of kernel k-means and k-means methods under certain condition.
  arXiv  Detail & Related papers  (2020-08-31T08:41:20Z)
• Adaptive Graph Auto-Encoder for General Data Clustering [90.8576971748142]
  Graph-based clustering plays an important role in the clustering area. Recent studies about graph convolution neural networks have achieved impressive success on graph type data. We propose a graph auto-encoder for general data clustering, which constructs the graph adaptively according to the generative perspective of graphs.
  arXiv  Detail & Related papers  (2020-02-20T10:11:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.