Double Self-weighted Multi-view Clustering via Adaptive View Fusion
- URL: http://arxiv.org/abs/2011.10396v2
- Date: Mon, 30 May 2022 08:02:20 GMT
- Title: Double Self-weighted Multi-view Clustering via Adaptive View Fusion
- Authors: Xiang Fang, Yuchong Hu
- Abstract summary: We propose a novel multi-view clustering framework Double Self-weighted Multi-view Clustering (DSMC)
DSMC performs double self-weighted operations to remove redundant features and noises from each graph, thereby obtaining robust graphs.
Experiments on six real-world datasets demonstrate its advantages over other state-of-the-art multi-view clustering methods.
- Score: 6.061606963894415
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multi-view clustering has been applied in many real-world applications where
original data often contain noises. Some graph-based multi-view clustering
methods have been proposed to try to reduce the negative influence of noises.
However, previous graph-based multi-view clustering methods treat all features
equally even if there are redundant features or noises, which is obviously
unreasonable. In this paper, we propose a novel multi-view clustering framework
Double Self-weighted Multi-view Clustering (DSMC) to overcome the
aforementioned deficiency. DSMC performs double self-weighted operations to
remove redundant features and noises from each graph, thereby obtaining robust
graphs. For the first self-weighted operation, it assigns different weights to
different features by introducing an adaptive weight matrix, which can
reinforce the role of the important features in the joint representation and
make each graph robust. For the second self-weighting operation, it weights
different graphs by imposing an adaptive weight factor, which can assign larger
weights to more robust graphs. Furthermore, by designing an adaptive multiple
graphs fusion, we can fuse the features in the different graphs to integrate
these graphs for clustering. Experiments on six real-world datasets demonstrate
its advantages over other state-of-the-art multi-view clustering methods.
Related papers
- DealMVC: Dual Contrastive Calibration for Multi-view Clustering [78.54355167448614]
We propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC)
We first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph.
During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels.
arXiv Detail & Related papers (2023-08-17T14:14:28Z) - 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) - 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) - 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) - Consistent Multiple Graph Embedding for Multi-View Clustering [41.17336912278538]
We propose a novel Consistent Multiple Graph Embedding Clustering framework(CMGEC)
Specifically, a multiple graph auto-encoder is designed to flexibly encode the complementary information of multi-view data.
To guide the learned common representation maintaining the similarity of the neighboring characteristics in each view, a Multi-view Mutual Information Maximization module(MMIM) is introduced.
arXiv Detail & Related papers (2021-05-11T09:08:22Z) - Auto-weighted Multi-view Feature Selection with Graph Optimization [90.26124046530319]
We propose a novel unsupervised multi-view feature selection model based on graph learning.
The contributions are threefold: (1) during the feature selection procedure, the consensus similarity graph shared by different views is learned.
Experiments on various datasets demonstrate the superiority of the proposed method compared with the state-of-the-art methods.
arXiv Detail & Related papers (2021-04-11T03:25:25Z) - ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete
Multi-view Clustering [59.77141155608009]
We propose a novel Auto-weighted Noisy and Incomplete Multi-view Clustering framework (ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression model.
ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our framework in different scenarios, thereby improving its generalization ability; 2) it automatically learns a proper weight for each view, thereby reducing the influence of noises; and 3) it aligns the same instances in different views, thereby decreasing the impact of missing instances.
arXiv Detail & Related papers (2020-11-20T10:37:27Z) - 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) - Consistent and Complementary Graph Regularized Multi-view Subspace
Clustering [31.187031653119025]
This study investigates the problem of multi-view clustering, where multiple views contain consistent information and each view also includes complementary information.
We propose a method that involves consistent and complementary graph-regularized multi-view subspace clustering (GRMSC)
The objective function is optimized by the augmented Lagrangian multiplier method in order to achieve multi-view clustering.
arXiv Detail & Related papers (2020-04-07T03:48:08Z)
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.