Multiple Graph Learning for Scalable Multi-view Clustering
- URL: http://arxiv.org/abs/2106.15382v1
- Date: Tue, 29 Jun 2021 13:10:56 GMT
- Title: Multiple Graph Learning for Scalable Multi-view Clustering
- Authors: Tianyu Jiang, Quanxue Gao
- Abstract summary: 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.
- Score: 26.846642220480863
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph-based multi-view clustering has become an active topic due to the
efficiency in characterizing both the complex structure and relationship
between multimedia data. However, existing methods have the following
shortcomings: (1) They are inefficient or even fail for graph learning in large
scale due to the graph construction and eigen-decomposition. (2) They cannot
well exploit both the complementary information and spatial structure embedded
in graphs of different views. To well exploit complementary information and
tackle the scalability issue plaguing graph-based multi-view clustering, 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 and well exploit
complementary information embedded in anchor graphs of different views by
tensor Schatten p-norm regularizer. Finally, we develop an efficient algorithm,
which scales linearly with the data size, to solve our proposed model.
Extensive experimental results on several datasets indicate that our proposed
method outperforms some state-of-the-art multi-view clustering algorithms.
Related papers
- High-order Multi-view Clustering for Generic Data [15.764819403555512]
Graph-based multi-view clustering has achieved better performance than most non-graph approaches.
We introduce an approach called high-order multi-view clustering (HMvC) to explore the topology structure information of generic data.
arXiv Detail & Related papers (2022-09-22T07:49:38Z) - Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph
Learning [15.617206773324952]
This paper presents an efficient multi-view clustering approach via unified and discrete bipartite graph learning (UDBGL)
An anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views.
The Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures.
arXiv Detail & Related papers (2022-09-09T08:51:01Z) - 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) - Multi-view Contrastive Graph Clustering [12.463334005083379]
We propose a generic framework to cluster multi-view attributed graph data.
Inspired by the success of contrastive learning, we propose multi-view contrastive graph clustering (MCGC) method.
Our simple approach outperforms existing deep learning-based methods.
arXiv Detail & Related papers (2021-10-22T15:22:42Z) - Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
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.
arXiv Detail & Related papers (2021-08-15T13:14:28Z) - Multilayer Clustered Graph Learning [66.94201299553336]
We use contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph.
Experiments show that our method leads to a clustered clusters w.r.t.
We learn a clustering algorithm for solving clustering problems.
arXiv Detail & Related papers (2020-10-29T09:58:02Z) - Learning Multi-layer Graphs and a Common Representation for Clustering [13.90938823562779]
We focus on graph learning from multi-view data of shared entities for spectral clustering.
We propose an efficient solver based on alternating minimization to solve the problem.
Numerical experiments on synthetic and real datasets demonstrate that the proposed algorithm outperforms state-of-the-art multi-view clustering techniques.
arXiv Detail & Related papers (2020-10-23T11:12:43Z) - Multi-view Graph Learning by Joint Modeling of Consistency and
Inconsistency [65.76554214664101]
Graph learning has emerged as a promising technique for multi-view clustering with its ability to learn a unified and robust graph from multiple views.
We propose a new multi-view graph learning framework, which for the first time simultaneously models multi-view consistency and multi-view inconsistency in a unified objective function.
Experiments on twelve multi-view datasets have demonstrated the robustness and efficiency of the proposed approach.
arXiv Detail & Related papers (2020-08-24T06:11:29Z) - 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.